Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of Control Systems for Robotic Spatial Planning Platforms

The work is devoted to the study of the control system of the moving platform of the manipulative robot. The purpose of the work is the development and research of an instrumental complex of control, analysis and modeling of the operation of positioning platforms based on a spherical mechanism for use in automated industrial systems. The state of use of multi-link positioning platforms in various industries was analyzed and their classification was provided, the main areas of use were determined. A comparative analysis of mathematical methods for describing the kinematic properties of positioning platforms was performed, namely: the closed vector contour method, the coordinate transformation method. A model with an analytical apparatus for controlling the drive links of the spherical mechanism according to the given scenarios of the orientation of the positioning platform was developed, as well as a mathematical model of inverse kinematics was made, the working space was modeled, and the service angles were determined, and a calculation and real-life experiment was conducted. For the first time, the work uses the dependencies of analytical geometry to implement algorithms for controlling a spherical mechanism by inverse kinematics, and offers software and hardware that combines visual modeling tools with real-time data transmission to a controller that controls the drivers of a spherical mechanism by direct kinematics. The result of the field experiment is the testing of the proposed control algorithm on the manufactured manipulator mechanism on the most common movements of the platform of the spherical mechanism and its exit to the extreme positions in order to determine the working space and the values of the service angles. A system of remote control of the spherical mechanism at the hardware level with an interactive interface and tracking of the positioning target has been implemented.
A risk-aware approach to stock portfolio allocation based on Deep Q-Networks

Reinforcement Learning techniques have shown a great potential in the active allocation of stock portfolios. However, state-of-the-art solutions show limited stability and fairly high sensitivity to volatile market conditions. To tackle these issues, this paper presents a new risk-aware approach based on Deep Q-learning Networks. It leverages Quantile Regression DQNs to mitigate the underlying market risks and an action branching architecture to effectively handle high-dimensional stock spaces. Furthermore, it also introduces noise perturbations to the network’s weights aimed at self-tuning the degree of exploration for each input dimension. Based on the empirical simulations, which were carried out on the Dow Jones-30 stocks over a three-year period, the proposed system performs better than state-of-the-art RL solutions in terms of cumulative return, stability, and sharpe ratio.
Application of Deep Learning Algorithm Based on Neural Network

Convolutional neural network is the most typical deep learning model and has become one of the most popular technologies in the field of computer vision. The purpose of this paper is to study the application of deep learning algorithms based on neural networks. Based on the clinical application of cough sounds, this paper reviews the research history and current situation of cough sound endpoint detection and recognition technology, introduces the process and purpose of voice endpoint detection, briefly describes deep learning and convolutional neural networks, and summarizes the fine-tuned Alexnet Convolutional neural network method, analyzes the relevant characteristics of cough sounds, and proposes a method for swine cough sound recognition based on spectrogram and fine-tuned Alexnet convolutional neural network. The 10-fold cross-validation simulation experiment shows that the method can effectively recognize the cough sound of pigs, and the three performance evaluation indicators of accuracy, precision and F1 score of the model reach 95%, 96% and 97% respectively.
Multi-information Aggregation Network for Fundus Image Quality Assessment

Fundus image quality assessment (IQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists. Existing fundus IQA methods mainly explore local information to consider local distortions from convolutional neural networks (CNNs), yet ignoring global distortions. In this paper, we propose a novel multi-information aggregation network, termed MA-Net, for fundus IQA by extracting both local and global information. Specifically, MA-Net adopts an asymmetric dual-branch structure. For an input image, it uses the ResNet50 and vision transformer (ViT) to obtain the local and global representations from the upper and lower branches, respectively. In addition, MA-Net separately feed different images into the two branches to rank their quality for supplementing the feature representations. Thanks to the exploration of intra- and inter-class information between images, our MA-Net is competent for the fundus IQA task. Experiment results on the EyeQ dataset show that our MA-Net outperforms the baselines (i.e., ResNet50 and ViT) by 3.06% and 7.61% in Acc, and is superior to the mainstream methods.
Relay Module IoT Devices for Remote Controlling of Home Automation System

Remote controlling of home automation system has the potential to help people to control the electronic and home appliance from anywhere. Internet of things (IoT) is proposed to provide the connect and control devices by using the internet network. IoT based switching relays can be used to easily disconnect and connect 220V AC voltage in the home. In this work, ESP8266 microcontroller with relays module to switch and control the home appliances. The internet signal from Wi-Fi is captured by the ESP8266, the microcontroller will send commands according to the instructions from the smartphone remotely. System testing is carried out to determine the performance of the system can work according to design. The three parameters in the system test are the response of the microcontroller to the smartphone application, the response of the relay to the microcontroller, and the response of the electrical device to the relay.
Design of a System for the Camera-Based Location Estimation Algorithm Accuracy Assesment

This evaluation method is done for understanding how accurate and how precise the attitude estimation in terms of position is. In order to achieve the evaluation goals, a system is made. This system consists of a MicroScribe Gx2 digitizer, a technical camera and a landmark. The landmark is in two dimensions of a flat PCB layout of infrared Emitter Diode (I.E.D). The I.E.Ds, called as feature points, are arranged in a matrix of 5 by 5. They are spaced each other in 40.6 millimeters. Many experimental results obtained with 1.4491 ± 0.8166 error in millimeters prove that the proposed method is applied quite well for attitude estimation evaluation and the attitude estimation algorithm is good as well.
Design and motion planning of a dexterous flexible cranial puncture robot

Exploring and promoting research on the basic laws of the human brain, related new technologies, and improving human brain health are important development goals of China's “Brain Science” program. Based on the goal, this paper designs a dexterous flexible brain puncture robot to help the exploration of deep brain physiological activities. The robot is mainly composed of a novel multi-valve bevel-tip needle and its driving mechanism. The novel design of needle is based on a bio-inspired flexible needle, retaining only the needle with nested structure, replacing the needle handle with a nickel-titanium wire attached to the tip of each valve. The new flexible needles are easier to make than needles nested together over the entire length of the handle. In order to control the puncture trajectory of the puncture robot in the complex brain environment, an algorithm which adds local conical space restriction to the sampling procedure to obtain smooth path is applied as the basis of path planning in this paper. As an improvement, the sampling points are selected according to the three position relationships between the target and the local cone sampling space in the algorithm, and the Informed-RRT* algorithm is used to optimize the search space. Simulation results show that the improved algorithm accelerates the speed of path planning and obtains shorter paths. It lays a foundation for the subsequent physical experiments and real-time path planning.
An Unsupervised Attack Detection Approach for Software Defined Networks

Software Defined Networking (SDN) is quickly becoming a vital technology for the future Internet. SDN provides a worldwide network with the capacity to manage network traffic dynamically. One of the main advantages of SDN over traditional networks is that it provides better network security due to centralised control. However, the flexibility offered by SDN architecture raises several additional network security concerns that must be addressed to improve SDN network security. This study proposes an unsupervised learning method to address attacks in the SDN controller. 7 extracted features from southbound traffic have been used to train KMeans, MeanShift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), AgglomerativeClustering, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), MiniBatchKMeans, Ordering Points To Identify Cluster Structure (OPTICS), and SpectralClustering, which are all well-known unsupervised classifiers. In terms of various well-known performance measuring criteria, such as Silhouette Score (SS), Calinski Harabasz Index (CHI), and Davies Bouldin Index (DBI), BIRCH outperforms all other classifiers.
Prescribed performance control using an adaptive super-twisting sliding mode method for quad-rotor UAV under the disturbance from variable-length gimbal

Recently, bridge detection by QUAVs(Quad-rotor Unmanned Aerial Vehicles) is more and more prevalent, however, there is a problem that QUAVs cannot fly close enough to get clear shots for some complex bridge structures. To solve it, an invention of variable-length gimbal mounted on QUAV is proposed in this study, at the same time, the control method of QUAVs with the gimbal is put forward. In detail, dynamic model of system is given considering the disturbance from gimbal and model uncertainty. Then, assuming the upper bound of the disturbance and uncertainty is unknown, an adaptive super-twisting fast terminal sliding mode control method meets prescribed performance is planned, which can achieve accurate estimation of the upper bounds. As a result, the validity of the method and its superior system performance is proved by simulation outcomes.
Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset

This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems.
Design and Simulation of a PV System with Battery Storage Using Bidirectional DC-DC Converter Using Matlab Simulink

PV (Photovoltaic) module consists of couple of solar cells in the series and parallel combination used to convert solar radiation into electricity. They are among the most well-known source of renewable energy. Due to the absence of hazardous emissions, solar energy is on par with fossil fuels in terms of the environmental benefits it provides. To build a PV system with battery storage, we employed a MPPT controller, that maximized the power output, a PI based voltage controller that maintained the voltage profile across the output. The simulation results showed that the system was able to meet the load requirement in the isolated mode. In addition to that use of energy storage devices and to support the battery a bidirectional DC-DC converter has been used in the paper. To managed the generated power across the renewable energy sources as well as to deals with the system uncertainty the requirement of energy storage systems also has been increased. The simulated result presented in the paper concluded that for various renewable energy sources like solar, wind, etc., batteries are an ideal energy storage solution to provide the support in the isolated DC system.
Transient simulation of nozzle geometry change during ablation in high-voltage circuit breakers

In high voltage circuit breakers, radiated heat from the electric arc is strongly absorbed by surrounding poly-tetra-fluro-ethylene (PTFE) parts and causes them to reach their vaporization temperature and ablate. Ablation deforms the PTFE parts, widens the throat and hence, changes the nozzle geometry. This paper presents a physical model coupling plasma flow simulation with erosion and movement of the PTFE walls to study this phenomena. The model is first applied and validated on a segmented tube case and on a model SF6 circuit breaker for which the experimental data is available. Then, the model is used to investigate the effect of the surface ablation on the nozzle geometry in a long-operation time.
A Forecasting Method for User-Side Power-Related Carbon Emissions

In order to help the power industry apportion the responsibility of carbon emissions and optimize the carbon emission reduction strategy, the prediction of carbon emissions related to power on the user side is studied. According to the method of predicting the power consumption first and then the electric carbon conversion, the carbon emission related to the user side can be predicted. The power consumption prediction of users is the core. A multi-modal embedded recurrent neural network is designed to model the short-term dependence of power consumption sequence considering the multiple influencing factors of power consumption; A historical attention mechanism is proposed to capture the periodicity factors in the power consumption sequence by considering the periodicity of users' power consumption habits. The experimental results show that the performance of the proposed method is significantly better than that of some commonly used power consumption prediction methods, which is helpful for the accurate prediction of power-related carbon emissions on the user side.
Volumetric Information Gain Guided Receding Horizon Planner for Active 3D Reconstruction

Active reconstruction has been used to mobile robots, unmanned aerial vehicles (UAVs) for active perception, inspection, coverage planning, etc. Usually, it is formulated as a next-best-view (NBV) problem. However, this kind of formulation doesn't consider long-term situations, so the robots are more likely to get trapped in dead zones, or to generate local optimal paths. Besides, current methods usually are only suitable for robots with unconstrained motions and low degrees of freedom. In contrast, for robots like mobile manipulators, the problem is high dimensional and suffers from reachability constraints at the same time. Meanwhile, for reconstruction task, the continuity constraint of adjacent views should also be considered. To overcome these challenges, this paper presents a view path planner (VPP) by using forward simulation. We iteratively build a sampling exploration tree, and the sampling process is guided by the volumetric information gain computed on each branch of the tree. The reachability and continuity constraints are explicitly considered when sampling new states, which guarantees the feasibility. To solve the myopic problem for NBV-based methods, this paper presents a receding horizon planner which works like the model predictive control (MPC). Finally, we conduct comprehensive experiments over different information gain metrics and their performance are studied in detail. The results show that the proposed method achieves superior performance in terms of reconstruction efficiency and coverage rate.
Stress Detection using Wearable Physiological Sensors and Machine Learning Algorithm

The primary objective of this methodology is to develop and test the performance of a wearable psychological sensor using EEG, EDA and ECG to observe the stress level of humans and analyze if the observed variation in psychological signals is related to the objective biomarker of stress. An integrated wearable system with physiological sensors is designed, developed, tested and evaluated in this paper for monitoring stress using biological markers in the working environment. Stress detection is performed using the Muse S (Gen 2) EEG headset, Savvy ECG sensor, and Shimmer3 GSR sensors. For each subject under test, 32 features are extracted from the multi-modal signals, of which, five features are retained for EEG, four signals for ECG and 1 for EDA, summing up to 10 features. The window size for the collection of each feature is one minute. Hence, for the 20 participants and ten features, a data of 200 minutes is used as training data in a controlled environment. Of these, 138 minutes are labelled as stressful tasks and 62 minutes are labeled as no-stress. The proposed stress detection model effectively assesses stress under a controlled environment with an accuracy of 97% and in an everyday environment with an accuracy of 93%. This work aims to design and develop a remote control system that can be used in medical devices to prevent the aftereffects of stress.
A Deep Learning-Based Channel Aware Single Step Signal Detection in Downlink Multi-User NOMA

This paper presents the design of a data-driven algorithm for a multi-user channel aware single step signal detection system in Non-Orthogonal Multiple Access (NOMA) wireless communication networks. In a NOMA downlink system, the Successive Interference Cancellation (SIC) decoding process can be performed at the receiver, where users are decoded based on their channel behavior. The SIC detection accuracy largely depends on the estimation accuracy of the Channel State Information (CSI) which is highly difficult in general due to the non-linear and non-stationary nature of the real wireless channels. Moreover, SIC performance is limited by the receiver complexity and error propagation problems. In this paper, we design a Deep Learning (DL) based NOMA receiver that considers jointly the CSI and detects the originally transmitted symbols of the multiple users. This approach differs from the existing NOMA receivers that use SIC decoding because our proposed DL-based channel aware single-step signal detection method allows each user to detect their symbols using their respective Deep Neural Network (DNN) in a single step. The DNNs are first trained offline using data generated from the channel realizations and labelled symbol data, and then they are tested in terms of recovering new transmitted symbols directly. Our simulation results show that the DL-based approach provides a better symbol detection performance than the SIC.
Wannier‐Stark Ladders and Bloch Oscillations in Superlattices

Conduction electrons in real crystalline solids behave very much like electrons in free space, moving in straight lines between collisions when subject to an electric field. But in an ideal (although cold) world, free from scattering by impurities, imperfections and thermal vibrations of the lattice, how would conduction electrons behave? That question, answered in principle long ago in light of the then newly developed quantum mechanics, was purely academic until recently.
Have Java Production Methods Co-Evolved With Test Methods Properly?: A Fine-Grained Repository-Based Co-Evolution Analysis

Any source code of a software product (production code) is expected to be tested to ensure its correct behavior. Whenever a developer updates production code, the developer should also update or create the corresponding test code to check if the updated parts still work correctly. Such a desirable co-evolution relationship between production and test code forms a logical coupling. Although the logical coupling is detectable through an association analysis on the code repository such as Git, the detection granularity is coarse because the conventional repository is at the file level. For observing those logical couplings as precisely as possible, this paper utilizes the finer-grained, Java method-level repository (FinerGit). Then the paper proposes a metric measuring the extent to which a production method has co-evolved with test methods and conducts a case study using ten open-source projects. The results show that most Java methods (98% on average) have co-evolved with test methods, but some have not; The proposed metric helps detect those methods having the potential risk that the developers might not test adequately.
Determination of Characteristic Gases Diffusion Coefficients in Transformer Oil

Due to various reasons, large power transformers will inevitably produce bubble impurities with characteristic gases such as hydrogen, carbon monoxide, carbon dioxide, methane, ethane, ethylene, and acetylene as the main components. The content of characteristic gases at different locations provides important information for the operating status of the transformer. In order to simulate the transport of characteristic gases, it is necessary to obtain their diffusion coefficients in transformer oil. In this paper, the basic principle of the bubble dissolution method was used to build an experimental platform to efficiently determine the diffusion coefficients of hydrogen, ethane and ethylene in transformer oil at $25^{\circ}\mathrm{C}$. , the measured values are $1.4\times 10^{-5}\text{cm}^{2}/\mathrm{s}$ for H2, $1.6\times 10^{-6}\text{cm}^{2}/\mathrm{s}$ for $\mathrm{C}_{2}\mathrm{H}_{6}$, and $1.1\times 10^{-6}$ cm ${}^{2}/\mathrm{s}$ for $\mathrm{C}_{2}\mathrm{H}_{4}$, with transformer oil degassed at 2 Pa for 20 min. It was found that inadequate degassing led to notably underestimated diffusion coefficients.
Automatic identification of Urdu fake news using Logistic Regression Model

Social media offers a platform to disseminate information with family and friends quickly. The spread of fake news on social media has a significant social and economic impact. With the ever-increasing amount of social media data, it is challenging to quickly differentiate between real and fake news. In previous years, the research community focused on Fake news classification for the English language. However, many resource-poor languages, such as Urdu, still require efficient methods to classify and contain fake news. This study proposes a methodology to identify Urdu fake news based on machine learning techniques. Our proposed methodology uses the TF-IDF feature extraction technique and Logistic regression classifier to classify Urdu fake news automatically. The proposed approach outperforms the baseline with a 72%f1 score.