Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability Analysis for Multiple-time Renewable Energy Forecast Error Using Conditional D-Vine

Probabilistic information of the forecast has become more important due to the ever-increasing penetration of renewable energy. However, most forecast methods are based on the point forecast that cannot provide valuable probability information. Meanwhile, the existing probability analysis methods are basically based on one-time intervals and only, which omits vital temporal correlation information. Thus, this paper proposes a novel conditional D-vine based forecast error analysis method that considers various dependence relations among sequential time intervals. Then, a Monte Carlo based method is proposed to compute the forecast error interval under different confidence levels. The case study on the actual wind farms’ forecast and actual output data in Ningxia, China, demonstrates the effectiveness of the proposed D-vine based conditional forecast error analysis method. The result shows that the proposed method is much more robust and provides more probability information than the Gaussian copula-based forecast error analysis method.
A Novel Forecasting Method for Short-term Load based on TCN-GRU Model

In order to improve the accuracy of short-term electric load forecasting and provide stronger assurance for the stable operation of the electric power system, a short-term load forecasting method, TCN-GRU, which combines time convolutional network (TCN) and gated recurrent unit (GRU) is proposed in this paper. This method comprehensively considers the timing characteristics and non-timing characteristics of the data. The short-term electric load prediction is realized by the TCN model to realize the further feature extraction of the time series features and the nonlinear fitting ability of the GRU model. Based on the electric load data of an industry in Nanjing, Jiangsu Province, the load forecasting ability of the TCN-GRU model is verified. Experiments show that the proposed method has a great advantage over the other deep learning methods.
A Negative Parallel Virtual RLC Damper for LC-filtered Constant Power Load in DC Microgrid

In DC microgrid, the impedance intersection of LC filter and constant power load (CPL) may cause the instability of the bus voltage, and one of the effective solutions is to increase the input impedance magnitude of CPL by inserting series-virtual resistance. However, large series-virtual resistance may lead to a poor performance of CPL to some extent. In order to overcome this drawback, this paper proposes a negative parallel virtual RLC damper to stabilize the whole system while not decreasing the performance of CPL. Furthermore, the impact of negative parallel virtual RLC damper on performance of CPL is analyzed. Finally, the proposed negative parallel virtual RLC damper method is validated in simulation.
LSTM-CNN-Based Transient Stability Assessment

In the face of huge challenges in security and stability analysis brought by the growing scale and complexity of modern power systems, the application of deep learning to the transient stability assessment (TSA) of a power system has become a topic with ever-increasing concerns from both academic and industrial communities. Aiming at improving the speed and accuracy of TSA as well as addressing the space-time features of electrical parameters in the transient process after disturbances, this paper proposes a TSA model based on the Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN). This model can not only extract the temporal features during the transient process, but also can attain the spatial features at different nodes. Also, the model is optimized through performance comparisons among different structures and parameters. The presented model is demonstrated by the IEEE 39-bus power system with high accuracy, and is of the potential for on-line TSA.
Short-term Load Forecasting Based on Multi-model Fusion of CNN-LSTM-LGBM

Inter-regional energy dispatch and regional peak cutting and valley filling require accurate load forecasting as support. In order to improve the forecasting accuracy, this paper proposes a multi-model fusion forecasting method based on CNN (convolutional neural network)-LSTM (long short-term memory)-LGBM (Light Gradient Boosting Machine) considering demand response. The CNN's ability is exploited to effectively extract local features, and LSTM’s ability to grasp time series information is used to build a serial CNN-LSTM model. Meanwhile, LGBM's regression analysis capabilities for nonlinear influencing factors is utilized to build an LGBM prediction model, and then an optimal combination method is used for model fusion. In addition, the impact of demand response, that is, electricity price factors, on regional loads is also considered. Through testing on the load data set, the results show that the fusion model has better load forecasting performance than individual model, and the MAPE (Mean Absolute Percentage Error) of the test set is 1.597%.
Research on WSN Routing Based on Pheromone Factor of Improved Ant Colony Algorithm

In order to achieve the security and efficiency of data transmission in Wireless Sensor Networks(WSN), this paper improves the routing protocol based on pheromone concentration factor in ant colony algorithm. By introducing the comprehensive trust degree, this problem is transformed into a combinatorial optimization problem to balance the relationship among node trust value, node energy and link quality, so as to find the comprehensive optimal path. Unlike ordinary wireless routing protocols, this paper also takes into account the security of routing transmission, the load of the overall system, and the stability of message transmission. At the same time, in the routing maintenance part, it can also make use of the trend of trust value slowly decreasing to quickly adjust the nodes with insufficient energy or low trust in the system. Through experimental test and analysis, the improved routing protocol greatly promotes the efficiency of data transmission in WSN, thus prolonging the network life cycle.
Signature Biometric based Authentication of IP Cores for Secure Electronic Systems

Intellectual property (IP) piracy has emerged as a potential hardware security threat in the last few decades. Growing usage of electronic systems in critical applications such as military and healthcare entails integrating only authentic functional blocks or IP cores into the system-on-chips (SoCs). The usage of only authentic IP cores can be ensured by detecting the designer’s secret information hidden into the IP core designs, thereby protecting from the pirated or fake IPs. This paper proposes first time the designer’s handwritten signature biometric based authentication of IP cores. In this paper, a digest of the designer’s signature biometric is generated using the proposed approach. Further, the digest of the signature biometric is mapped into the corresponding hardware security constraints to be implanted into the IP core design during the behavioral synthesis process. The presence of designer’s signature biometric into the IP core design ensures unique identification of the genuine vendor during authentication. The robustness of the proposed approach has been measured using a probability of coincidence metric based security analysis. Finally, the results reveal that the proposed approach yields higher security at negligible cost overhead.
The Preliminary Conception of Access Protection for Distributed Generation Based on the Internet Communication Technology

To approach the national strategy about the "3060" dual-carbon, increasingly distributed power sources need to be connected to the distribution side. Because of the access of the distributed power grid, the original closed environment of the isolation protection in the power system has been broken. However, the current research is focused on the general protection technology in the traditional power grids. The protection technology of power systems is facing a difficult situation. On the one side, the system environment assumes keeps isolation and a close loop. The system will not be able to fully absorb the power from the distribution grids. On the other side, the system will suffer network attack risks because it accesses the internet communication with distributed power grids. The environment of communication is open and no strict security measurement. Therefore, this paper proposed the preliminary concept of the distributed power grid. Firstly, the characteristics and security loopholes of the distributed power grids control system based on internet communication are analyzed. Then, the characteristics and defects of the security protection technology based on Internet technology are analyzed. Finally, the preliminary conception of the protection scheme is proposed.
Intrusion Detection Method Based on the Legality of Action Messages in Process Layer of Smart Substation

According to the characteristics of the relationship between trip messages and fault components in the smart substation, a method for detecting the legality of the action message in the process layer of the smart substation is presented. The logical relationship between the protection information, the tripping message and the fault components is described, and then a cyber-physical fusion-based detection method is developed. The method has been verified by field test to detect abnormality and cyber-physical attack for circuit breaker action messages.
Improve the Application of XGBDT in Network Abnormal Traffic Detection

Detecting abnormal traffic in real life often requires analyzing massive data (high-dimensional data) and unbalanced data. Aiming at the above problems, an intrusion detection model (SMBR-XGBDT) based on the combination of SMOTE algorithm and Boruta algorithm with Extreme Gradient Boosting (XGBoost) algorithm is proposed. The experiment selected 14367 extremely unbalanced samples based on the CIRA-CIC-DoHBrw-2020 data set, and detected 4 categories: DOH, Non-DoH, Benign-DoH, Malicious-DoH, using decision tree algorithm, random forest Algorithm, XGBoost algorithm as a control. The experimental results show that the SMBR-XGBDT model is significantly better than the other three models. The precision, recall, and F1 scores of the overall test were 93%, 93 %, and 93 %, respectively, which verified the effectiveness of the method. The precision rates of DOH, Non-DoH, Benign-DoH, Malicious-DoH were 88%, 100%, 98%, and 87%, respectively, which verified the feasibility of the method to deal with unbalanced data.
Automated Home Security using Facial Recognition with IoT Remote Monitoring

With the world moving forward internet of things and remote-control solutions it is natural that consumers want to implement home security with IoT technology and remote access and security protection for the same. Certainly, there are many products in the real world with the same implementation while some have failed miserably due to lack of security and easy hacking of data in mainstream day-to-day use. So, a new technique to protect homes using existing technology so the general user could make one's property safer and which will also work well in a real-world environment. The proposed work is to emulate the idea of a blockchain database. It is a decentralised database that is proved in the paper to be very effective and secure for almost any cloud computing process. The idea presented is to use facial recognition to automate a security system, which works by recognising users face data and allows security functions to happen without any hindrance. The proposed system has a camera which recognises the face of the user who is trying to enter the building or pass through a door and if they are in the database, they are allowed passage. If the system doesn't recognise the supposed user, it marks the user as an intruder and doesn't allow them passage and passes a message to the owner about the intruder with their photo, using internet connectivity. To implement two-stage protection automatic system shutdown and lockdown is implemented which makes the system open only after the user resets the system to use again, the user does not have to reset everything from the beginning as the previously stored data will not be deleted. The system is going to use a Raspberry pi micro-computer to do all the processing needed for the system to work. Since this is a prototype working project, only generally available technology and methods is used instead of sophisticated instruments.
Review on voltage regulation techniques via flexible resources in power distribution systems

Distributed generations (DGs), especially rooftop Photovoltaic (PV) generations are widely installed in users’ side in recent years. Meanwhile, electric vehicles (EVs) are getting popular all over the world. With the growing penetration of DGs and EVs, a distribution system's range of voltage variation becomes wider. It will bring more difficulties on voltage regulation issues for distribution system operators (DSOs). Besides the traditional voltage regulation devices, utilizing flexible resources, such as PV, EV, and controllable loads for voltage regulation can supplement DSOs. This paper performs a literature review of the development, impacts, and techniques to deal with the voltage regulation problems. The impact of voltage profile of distribution systems with high penetration of DGs and EVs is first analyzed, and then traditional voltage regulation methods are presented. After that, an overview of voltage regulation methods utilizing flexible resources is introduced.
An adaptive knowledge distillation algorithm for text classification

Using knowledge distillation to compress pre-trained models such as Bert has proven to be highly effective in text classification tasks. However, the overhead of tuning parameters manually still hinders their application in practice. To alleviate the cost of manual tuning of parameters in training tasks, inspired by the inverse decrease of the word frequency of TF-IDF, this paper proposes an adaptive knowledge distillation method (AKD). This core idea of the method is based on the Cosine similarity score which is calculated by the probabilistic outputs similarity measurement in two networks. The higher the score, the closer the student model's understanding of knowledge is to the teacher model, and the lower the degree of imitation of the teacher model. On the contrary, we need to increase the degree to which the student model imitates the teacher model. Interestingly, this method can improve distillation model quality. Experimental results show that the proposed method significantly improves the precision, recall and F1 value of text classification tasks. However, training speed of AKD is slightly slower than baseline models. This study provides new insights into knowledge distillation.
Forecasting Methods of Big Data Based on Machine Learning and Cloud Computing

The history of credit cards could date back to 1950s. Credit card gradually become one of the main forms of payment for people. In this case, the default risk has become one of the biggest problems in credit risk and how to identify this risk is more and more important. This report aims to solve the problem about identification of default risk in bank before the credit extension or other service. The main solutions for this problem at present are setting black and white list, fixed threshold filtering. However, these two solutions have a lot of drawbacks: 1. They cannot be used to deal with a large number of loan transactions. 2. They take a lot time to complete estimation, which is not efficient. 3. The accuracy of these methods is low. Based on these drawbacks, I put forward methods by using machine learning to predict whether the borrowers will default.
A Gesture Recognition Method Based on Yolov4 Network

With the diverse development of human-computer interaction. The gesture recognition-based interaction has a large-scale application prospect in collaborative robotics and smart home control. However, due to the similarity of gestures and occlusion, the previous methods have problems with the poor accuracy and shift of detection box. Aiming at the above issues, a gesture recognition method based on the Yolov4 deep learning algorithm is proposed. Firstly, gesture images were collected and annotated, and the data was processed by the GridMask and scale adjustment of data augmentation in order to improve the generalization performance of the network. Then K-means clustering algorithm was used to cluster the annotation boxes in the annotation dataset, by this way, the anchor box of YOLOV4 was optimized to improve the IOU accuracy. Finally, during the training process, focal loss and Consine warmup were adopted to improve the unbalanced sample number of classes and overfitting of the network. The experimental results shows that the proposed algorithm outperforms the main target detection models which include Yolov4, Yolov3 and Faster RCNN, the average recognition accuracy of this method reaches 99.4% and the FPS is 33fps. The proposed algorithm has good real-time performance.
A Method for Evaluating the Accommodation Capacity of Distributed Energy Resource

With the continuous injection of new energy into the power grid, in order to ensure the safe and stable operation of the power grid, analysis and evaluation of the power grid's ability to accept distributed power sources. First, analyze the limiting factors of the distributed power supply capacity in the power grid; on this basis, quantitatively describe the limiting factors, build an evaluation model for the distributed power supply capacity, and find the optimal solution strategy for the solution model, and give the evaluation results of distributed power sources at the designated node of the power grid at the same time ; finally, on the basis of the above research, strategies and measures to improve the capacity of distributed power sources are given.
An Adaptive Method for Classification of Noisy Respiratory Sounds

Respiratory sounds (RSs) contain essential information about the physiology and pathology of lungs and airways obstruction. Therefore, RS understanding has a critical role in diagnosing respiratory patients. However, the external noise in the respiratory sound signal is a large restriction for this study. In this paper, we propose a method to classify noisy respiratory signals. Firstly, four adaptive filtering algorithms (RLS, LMS, NLMS, and Kalman) are applied and evaluated for noise reduction. Then, we extract features of filtered sounds, using Mel Frequency Cepstral Coefficient. Finally, the SVM method is used to classify respiratory sounds. We also conducted experiments on a dataset consisting of 1980 breath events, collected from 16 healthy volunteers. The obtained results show that, the investigated methods, SVM and Kalman achieves the highest accuracy of 95.5%.
Dual-Input Single-Output Non-Isolated DC-DC Converter for GIHEVs

In most cases, battery-powered electric vehicles rely heavily on the available power system for their battery charging. This leads to excessive stress on the distribution network of the power system. From the name, grid-independent hybrid electric vehicles run independently by utilizing multiple renewable energy resources by forming hybrid energy systems. In this paper, a dual-input single-output (DISO) non-isolated dc-dc converter with high step-up voltage magnitude is presented for GIHEVs (grid-independent hybrid electric vehicles). Additionally, to improve the output voltage magnitude, voltage multiplier (VM) stages are used with help of diode-capacitor (D-C) combination. Because of this converter, GIHEVs benefit from a minimalist structure, fewer switches, light-weight, high gain, minimal voltage stress, and greater efficiency. To clarify the converter functioning, we stroll via the modelling of a 1000W dual-input single-output model and, also concentrate on the losses and efficiency computations. The fuel cell source of 36V and solar source of 48V is chosen to design the system of 1000W. We uphold the converter design and performance with MATLAB/Simulink model that validates the mathematical computations.
Deep Reinforcement Learning Based Coordinated Voltage Control in Smart Distribution Network

This paper designs a reactive power optimization strategy based on multi-agent deep reinforcement learning soft actor-critic (MASAC) algorithm. Compared with the traditional method, our proposed method does not depend on accurate power flow modeling based on the data prediction of day-ahead load and distributed generation data. The proposed strategy uses deep neural network to train the action functions of solar photovoltaic (PV) and wind turbine (WT) inverters as multiple agents and completes the training of deep neural networks in the process of interaction with the distribution network environment. Additionally, centralized training and decentralized execution framework are adopted to solve the Volt-VAR control problem. In our MASAC algorithm, each agent is regarded as an actor trained with a critic during the training process. Finally, a numerical study is given to verify the effectiveness of the MASAC algorithm.
A Review of Channel State Information in Massive MIMO for 5G and B5G

In this paper, the Massive multiple-input multiple-output systems which have lately gotten a lot of press because they can harness a lot of antennas at the base station (BS), maybe even more than the number of user equipment (UE) terminals in the cell was reviewed. Massive MIMO may provide massive gains in spectral efficiency as well as aid in minimizing intercell interference, offering immunity to small-scale fading (known as the channel hardening effect), and providing immunity to small-scale fading (known as the channel hardening effect) with simple linear precoding and combining (known as the channel hardening effect). As a result, obtaining Channel State Information (CSI) in a reliable and efficient manner is a critical challenge for fully exploiting the potential benefits of mmWave large MIMO systems is especially important in ensuring the practical deployment of massive MIMO. Various techniques are being deployed like Artificial Intelligence (AI) and Compressive sensing techniques (CS).
Research on the smart tower of transmission line based on the architecture of distributed regional autonomous system

The distributed transmission line management mode has the advantages of reducing the amount of data processed by the management center, reducing the emergency processing time and improving the data processing efficiency, so as to improve the operation reliability of the transmission line. This paper presents the concept of smart tower based on the architecture of distributed regional autonomous system for transmission lines, and defines the topology and functional framework of the smart tower. By calculating the accuracy of sensing data with different numbers of sensing devices installed on the transmission line, the layout principle of sensing devices is studied. Moreover, the smart linkage scheme between the smart tower and the management center is further proposed. The research provides support for the transformation from the traditional transmission line management mode to the smart transmission line management mode.