Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Evaluation of Eco-environmental Quality in Guanzhong Urban Agglomeration Based on Multi-source Remote Sensing Data

With the rapid development of social economy in Guanzhong urban agglomeration, the living standards of the people has been greatly improved. Because of the extensive mode of development, environmental problems have also become the cost of economic development in Guanzhong urban agglomeration. Urban agglomeration is facing a series of environmental problems, such as ecological destruction, atmospheric pollution, water shortage and so on. Focusing on these issues, multi-source remote sensing data and auxiliary data are integrated to build a comprehensive and regional eco-environmental quality assessment model. The evaluation model is established based on the combination of Fuzzy Analytical Hierarchy Process, Principal Component Analysis and Lagrange Multiplier. The results show that the quality of ecological environment in Guanzhong urban agglomeration shows a downward trend in general. The area of the eco-environmental quality index between 0.4-0.6 performs an upward trend, which transformed from other levels. The quality of ecological environment in Guanzhong urban agglomeration has gradually improved from north to south. The southern part of Guanzhong urban agglomeration is Qinling Nature Reserve, which Contains a lot of woodland and has high ecological environment quality.
Load Management of Economic Power Dispatch using Optimization Algorithm

Economic Power Dispatch is a critical component of the power system network (EPD). It assigns required load to online producing units while accounting for system constraints. The fundamental purpose of EPD is to lower overall power generation costs. For example, the bi-objective Combined Emission- Economic Dispatch (CE-ED) problem addresses the environmental impacts of fossil fuel power plant emissions. The bi-objective challenge reduces both fuel costs and emissions. Generators are simulated using quadratic cost functions to match their output restrictions. The non-linear, non-convex objective functions and equality and inequality requirements make the EPD issue more feasible. Recent evolutionary algorithms have improved results, especially for complex optimization problems. These methods identify global optimum solutions in non-convex and large solution domains. Recent algorithms search the solution space at random utilising numerous candidate solutions. Evolving algorithms must be able to get answers by exploring the feasible zone rather than researching the complex region. So the optimization process is speedier and consumes less calculation time while still finding the global optimum. The traditional technique fails due to premature convergence.
A Study on Guaranteed Privacy using One-time Identification Code on Digital Health Keeper

Recently, epidemiological investigation technology for identifying infected persons based on smart phone location data has been used to prevent threats by quickly finding close contacts who may be in the early stage of infection. In addition, in order to prevent the spread of COVID-19, the technology is used for rehabilitation through video call-based EEG, ECG, and EMG sensor-based treatment support, thereby preventing close contacts with infected people and protecting medical staff and patients. In most cases, there exists security and privacy concerns. This paper studies pseudonymization to protect security and personal privacy. The core of the technology proposal to enhance security and privacy in the loT and sensing-based medical technology environment is to approach the NFC network tagging-based OTAC authentication technology from a completely different perspective. This paper suggests a new service direction that can be used in the development of a system that protects personal security and personal information. The proposed technology is valuable to security and privacy.
EM Design and Performance Analysis of Dualband FSS based on SIW Cavity Technology

This paper presents a design of dualband frequency selective surface (FSS) element design based on SIW cavity (SIWC) technology and its performance analysis. The proposed dualband FSS is modelled using a combination of two FSS elements. A cross-slot element has been enclosed by a square slot to design the proposed shape. The structure shows dualband response at two different frequency bands C- and Ku-bands with sharp roll-off performance characteristics. The performance of the structure has been estimated using commercial CST Microwave studio. The simulation results show the higher selectively, very good isolation between the bands and stable performance characteristics compared with its equivalent conventional planar version.
High Performance Energy Efficient CMOS Voltage Level Shifter Design

This paper presents an energy efficient voltage CMOS voltage level shifter. Voltage level shifter is used for multi-supply design applications. The main purpose of voltage level shifter is to convert the voltage level from one level to another. We verified our voltage level shifter in ASAP7 7nm Fin-Fet technology. The proposed voltage level shifter is based on differential cascade voltage switch logic, which takes an input voltage in the range of 0.25V to 0.6V and provides an output of 0.7V. Our voltage level shifter improves propagation delay and power dissipation with 48% and 43%, respectively, with recently reported Wilson current mirror voltage level shifter with Zero-Vth design. The proposed design technique comes up with significantly lower power consumption and drastically reduced propagation delay over a wide range of temperatures (-25 to 25 degree Celsius), as compared to existing technologies.
Motion Transfer & Person Image Synthesis

Human motion transfer from a video to an image is one of the applications of GAN. This paper includes the techniques and GAN Models which transfers motion from a video to an image and produces digitally synthesized video of the person present in the image. This paper provides the insight of the Nested GAN model. It generates the human images along with the human features, pose, garments, etc., that is present in the wide source of images. The proposed method consists of two stages. First, the motion patterns are extracted from driving video sequences. Then image-to-image transformations are learned by maximizing the mutual information between the source image and the target image while simultaneously minimizing the distortion associated with the image-to-image transformations. Simulation results show that using our method reduces work factor values, and generates higher quality videos. Method discussed in this paper enables the generation of more realistic output images as well as provides the separated un-annotated attributes of the images. The suggested method along with some innovation outperforms the latest pose transfer and image synthesis models according to experimental results.
LSTM Neural Network with Attention Mechanism for Stock Timing in Quantitative Trading Field

With the rapid development of the stock market and computer technology in China, quantitative trading has become the most popular way of investment. Stock timing strategy based on stock price prediction or stock trend prediction is one of the quantitative investment strategies in quantitative trading field. There are many previous studies that achieve good results in stock timing with the development of machine learning and deep learning. LSTM is the most widely used method in these studies because it solves the problems of gradient disappearance and gradient explosion in long time series data in RNN models. However, if the input time series data is too long, the performance of LSTM will deteriorate rapidly. Therefore, we add the attention mechanism to the LSTM model, which can effectively solve the gradient problems and improve the accuracy of prediction. The main contribution of our research work is that we propose an effective stock timing model based on LSTM and attention mechanism. The experimental results show that the LSTM based on attention mechanism model outperforms LSTM model in terms of the RMSE and SMAPE.
Graph Based Texture Pattern Classification

Textures in 3D meshes represent intrinsic surface properties and are essential for various applications, including retrieval, segmentation, and classification. However, it is distinct from other types of 3D object analysis. The primary objective is to capture the surface variations induced by multiple textures. While numerous classical approaches are published in the literature, only a few work directly on 3D meshes. Given the versatility of graph representations, we propose a graph learning-based approach for classifying the texture of each facet in a 3D mesh. First, a three-dimensional mesh is transformed into a graph structure in which every node is a facet of a given mesh. Further, each facet is described by a feature vector computed utilizing the neighboring facets within a radius and their geometric properties. The graph structure is then fed into a graph neural network, classifying each node as a texture or non-textured class. The proposed technique has been validated using texture patterns from SHREC'18 and demonstrated positive performance.
Crack Detection and Classification for Reinforced Concrete Structures using Deep Learning

Building cracks such as gaping cracks, separation, and horizontal cracks are a few types of cracks that possess a severe issue on reinforced concrete; hence, the earlier the detection the cheaper the repairs. Numerous studies about crack detection considered VGG16, and Faster R-CNN because the severity level is crucial to each construction company business owner. This paper aims to build a deep learning model using Yolov3 that can detect a crack in reinforce concrete structures and categorize a medium, severe, or very severe crack using an android application. An android application was developed instead of using an expensive Ultrasonic Pulse Velocity in the market to detect the severity of the crack on the concrete. The overall accuracy summary of the android application is 93.33%, while the kappa value is. 97. Therefore, the deep learning model and android application produced an accurate calculation in detecting the crack and determining its crack classification.
AutoSleepNet: A Multi-Signal Framework for Automated Sleep Stage Classification

Sleep is one of the most vital yet often underrated components of human health. Sleep studies are crucial for unearthing various abnormalities associated with sleep, widely prevalent in today’s world and bound to increase over the years, owing to an increasingly rapid lifestyle, making short sleeping hours surprisingly too common. Sleep deprivation can heavily impact humans and their quality of life. Diagnosing sleeping issues accurately during the initial stages is one of the significant challenges faced by the medical community. Sleep stage scoring is considered the primary step in detecting sleep abnormalities or dividing a person’s entire sleep duration into different categories according to muscle movements, brain activity, eye movements, etc. Polysomnography is the scientific test that records these human activities during sleep through electrodes connected to patients. A hypnogram that results from this test is the graphical form of the sleep scoring done by technicians. For ages, this process has been carried out manually, which is frequently prone to error, requires ample time, effort and training, and is susceptible to inter-scorer differences. Therefore, it is important to devise an automated system for sleep staging. This experimental study involves Machine Learning techniques to classify the different sleep stages. Three important classifiers, namely, Random Forest, KNN, and SVM, have been used for this work. The model takes input data from a vast base of patients suffering from various sleeping disorders. The final output of the model is a hypnogram presented to the user, which contains the classification of every 30 seconds of sleep duration into one of the six sleeping stages (Wake stage, S1, S2, S3, S4, and REM). The overall accuracies achieved by the model range from 74.07% to 83.65%, with KNN delivering the best results.
A New H∞ Filter for a Nonlinear Singular System in the Time Domain

During design, control, identification and diagnosis of nonlinear systems, the decomposition of the system into sub-models is quite fundamental. This paper focuses on the state filtering of a nonlinear system represented by the classical multi-model based approach.Unlike the multi-model in a decoupled state, this model typically presents state-identical sub-models. By a set of linear matrix inequalities (LMI), we can guarantee the convergence of the error estimation.
CNN-based Identification of Parkinsonian Gait using Ground Reaction Forces

Parkinson's disease is among the most common conditions faced by older people, which causes stiffness and slowing of movement. One manifestation of the disorder assumes a non-physiological walking pattern characterized by flatfoot strike or toe-to-heel progression. This paper proposes a method to identify Parkinson's disease using Convolutional Neural Networks to classify the ground reaction forces exerted during walking. Bilateral plantar pressure is acquired using three Aidong IMB C20B pressure sensors for each foot. The training database consists of 13 records acquired in clinical environment and an additional 72 records publicly available online in the Gait in Parkinson's Disease database. The signals are pre-processed to obtain 2D images, which are used to train three Convolutional Neural Networks models: MobileNet, ResNet50 and DenseNet121. According to the performance metrics, the results obtained to identify Parkison's disease are remarkable. The best accuracy is 89% and is obtained for the MobileNet model.
Enhanced Dynamic Characteristics Of Bidirectional DC-DC SEPIC/ZETA Converter Based BESS

In the current scenario, much research is being done on renewable energy generation, electric vehicles, etc. In such systems, a battery energy storage system (BESS) is utilized to make functioning more reliable. For voltage management of a dc bus, this paper suggests a charging/discharging system using a BESS. In this paper, a bidirectional dc-dc SEPIC/ZETA converter with PID (proportional-integral-derivative) controller is demonstrated. The converter's state-space circuit averaged modelling-based transfer function approach is presented to control the switching pulses of the converter for SEPIC and ZETA modes. The controller tuning is presented by the classical Ziegler-Nichols method and PSO (Particle Swarm Optimization) tool. The operation of the system with bidirectional power flow control has been simulated and verified through MATLAB/SIMULINK results in both charging and discharging states of the battery under varying circumstances.
Multimodal Based Automatic Feeding Robotic Device

The automatic feeding robot is designed to solve the problem of self-feeding for the disabled and has a wide range of application prospects in the fields of medical care and social elderly care. However, most of the existing feeding robotic devices are limited with fixed operation and cannot fully address the various needs of the disabled. The systems usually can only complete tasks within preset procedures and lack of the interaction process with the disabled. In this paper, we develop a multimodal based automatic feeding robotic device. It consists of a speech recognition module, a visual perception module, and a interaction control module. The speech recognition module provides an entrance for users to interact with robots. Users can make their own decisions about the order and speed of eating through speech interaction. The visual perception module utilizes the received RGBD images to detect food and faces, and localize them in the real world. The final interaction control module is to control the robot to deliver food to the human mouth. We evaluate each module separately, they all reach the level of daily use. And then we conduct human feeding experiments in a real robotic device. In 30 attempts, the feeding success rate reached 83.3%.
Research and Application of Semi-Supervised Entity Recognition Method in The Field of Technology Policy

In the field of technology policy, a large number of technology policies are released every day, and scientific researchers need to always pay attention to a great number of technology policy information on different websites, and it is arduous to find crucial policy information from them. Using named entity recognition technology to convert a great number of unstructured text information in technology policy fields into structured information can help scientific researchers obtain crucial policy information. Compared with named entity recognition in the general field, the main challenge of entity recognition in the professional field is that there is less data in the professional field with annotations. In order to reduce the resource overhead of annotated data, a semi-supervised learning method for named entity recognition is produced. The advantage of the semi-supervised learning training model is that it can use the text data with label information and the text data without label information to train the recognition model, and improve the generalization ability of the named entity recognition model. This paper innovatively proposes a dynamic adversarial training method DAT (Dynamic Adversarial Training) that dynamically adjusts the loss weights of supervised data and unsupervised data, and applies it to semi-supervised entity recognition tasks, and proposes the DAT-Bert-CRF model. Effectively solve the problem of semi-supervised entity recognition. The result of our experiment show that compared with other semi-supervised entity recognition methods, the performance of our model in this paper is better.
Using SYBR Stain for the Assessment of Removal Efficiency and Continuous Detection of Viruses in Membrane Bioreactors

This report contains a novel method for rapidly detecting and measuring viral concentrations in wastewater. A fluorescent DNA and RNA stain (SYBR Gold) was used to stain viral material in membrane bioreactor effluent and detect it by measuring fluorescence after excitement with a fluorometer. A standard curve was developed to determine a relationship between fluorescence and virus concentration. The method was tested using membrane bioreactor influent and effluent, prefiltered with 0.2 micron filters. Viruses were then concentrated for measurement using centrifugation. The permeate from the membrane for each sample was used as a background measurement. A plan for a pilot system designed for a wastewater treatment plant was created from a small stream of effluent passed through ultrafiltration and dosed with SYBR for measurement in an in-line fluorometer. Lab testing suggested a possible critical control limit of 1.75×105 viruses/mL and assessed a membrane bioreactor an LRV of 2.3.
Machine-learning Approach to Microbial Colony Localisation

Due to the massive expansion of the mass spectrometry, increased demands for precision and constant price growth of the human labour the optimisation of the microbial samples preparation comes into question. This paper deals with designing and implementing an image processing pipeline that takes an input in the form of a Petri dish image with cultivated colonies and outputs the position of possible sampling points. In total 547 samples were collected. The first block of the pipeline consists of a trained customised ENet model which predicts a binary mask. Architectures U-Net, UNet++ and ENet were examined, where ENet was found to perform with the highest Dice coefficient (0.979).
A Risk Based Approach for Privacy Compliant Machine Learning Lifecycle

Data Protection and Privacy is a priority focus for each organization, driven by rapidly emerging regulatory requirements around the world. These regulations are generating continuous need of review and adaption across software used by business. Over the past years, usage of Machine Learning for businesses increased significantly. The increase of usage is giving significant advantages to the business with its capabilities of predicting the results, enables the business to prepare for the upcoming challenges. Core principle on which Machine Learning is based on is “Predictions are more accurate when the models can be trainable on huge amounts of data from various sources”. However, the need of processing large amounts of data is also bringing in a concern in terms of Data Privacy. With this paper, we will provide a risk-based approach, following the privacy focused threat modeling to ensure data privacy compliant machine learning lifecycle. Risk based approach advocates “Start Early” to identify potential risk early to plan and mitigate. We will describe in this paper, how risk-based approach can systematically help identifying usage of personal data, data minimization and flow and process implementation required to ensure the machine learning business case is meeting the data privacy regulatory requirement.
Autonomous Underwater Robotic Grasping Research Based on Navigation and Hierarchical Operation

This paper proposes a new framework for the autonomous underwater operation of underwater vehicle manipulator systems (UVMS), which is modular, standardized, and hierarchical. The framework consists of three subsystems: perception, navigation, and grasping. The perception module is based on an underwater stereo vision system, which provides effective environment and target information for the navigation and grasping modules. The navigation module is based on ORBSLAM and acoustic odometry, which generates the global map and plans a trajectory for the first initial stage. The grasping module generates the target grasping pose based on the extracted point cloud and the current robot state, and then executes the grasping task based on the motion planner. The proposed system is tested to perform several underwater target grasping tasks in a water tank, demonstrating the effectiveness of the system.
Quad-Band Petal Reflector Antenna for Ground Communications

Future protected communications require soldiers on the ground to carry several antennas to integrate with their man-packs to provide secure and reliable communications at several frequency bands. This paper describes a novel “petal-reflector” that combines four antennas into one using a quad-band antenna design. A dual-reflector antenna using axially displaced ellipsoid (ADE) sub-reflector fed with a wideband quad-ridged horn provided Ku-band transmit, Ku-band receive, K-band receive and Ka-band transmit frequencies using a high performance quadraplexer. The main reflector is made of six identical petals that can be quickly assembled and disassembled in the battle field. The antenna is assembled on a light-weight 2-axis gimbal to provide beam scanning over +/− 900 in elevation and 3600 in azimuth. The antenna / gimbal combination is then mounted on a tripod structure for field operations and uses commercial off the shelf (COTS) components and 3-D manufactured parts to minimize cost and weight. Details on the antenna design, analyses, hardware, integration and test results are presented.
Neural network classification of coffee varieties on hyperspectral images

In the presented work, an approach to the classification of high-resolution hyperspectral images in the problem of identifying coffee varieties is proposed. A spectralspatial convolutional neural network with compensation for lighting variations is used as a classifier. To compensate for the flare effect, an approach is proposed with a statistical assessment of the formation of the training sample. High accuracy of the proposed approach in the problem of classifying hyperspectral images of coffee varieties obtained with a scanning hyperspectral camera is shown.