Miracle-dz/UniFAR-FTUs huggingface dataset:
Day-ahead Prediction of Wind Power Based on Conditional Generative Adversarial Network

Short-term forecasting of wind power plays a critical role in day-ahead dispatching and economic operation of power systems. In this paper, a wind power prediction model based on Convolutional Neural Networks (CNN) and Conditional Generative Adversarial Network (CGAN) is proposed. With weather factor labels helping, the model is proposed to solve the problem of day-ahead prediction. In this model, K-means is applied to divide the historical wind farm data into several parts based on weather factors. At each time, the power of similar weather time can be found through grey relational analysis (GRA), which can be used as a label together with weather information. With the guidance of conditional labels, the generative model can generate samples more purposefully, and the discriminator can identify more accurately. By this way, the generator and the discriminator form a game so that CGAN model can improve the accuracy of wind power forecasting. Finally, three examples of Datang Hongxing Wind farm are given to test the validity of the proposed algorithm.
Research on identification algorithm of voltage sag type based on Adaboost-BP

Accurate identification of sag type is the basis of effective governance sag problem. This paper proposes a method for identifying voltage sag types based on Adaboost-BP. The Adaboost-BP classification model is a weighted combination of multiple weak classifiers to classify three types of sags: short circuit, induction motor starting, and transformer switching. Combined with the judgment result of the number of sag phases, the identification of the final sag type is realized. The results of the calculation examples show that the algorithm proposed in this paper can achieve more than 90% accuracy for each type of sag, and its performance is superior to the existing algorithms.
Research on Multi-agents Distributed Transactions in Distribution Network Based on ADMM

Aiming at the electricity transactions between prosumers in the distribution network level, a multi-agent peer-to-peer (P2P) transaction algorithm in the distribution system is proposed. In order to fully protect the privacy and security of each agent, this paper firstly builds a P2P transaction model between prosumers. At the same time, this model takes the need to collect grid usage prices into account and further the alternating direction method of multipliers (ADMM) is used to decompose the model in order to realize distributed transactions. Different numbers of prosumers and grid usage prices are selected as test cases. The results verify the convergence speed of the model, and the model can fully protect the privacy of prosumers.
A 0.3-V High Linear Rail-to-Rail Bulk-Driven OTA in 0.13 μm CMOS

A new solution for an ultra-low-voltage (ULV), linear operational transconductance amplifier (OTA) is presented in this brief. The circuit is based on a non-tailed, classAB differential stage, combined with an additional linear resistor, which suppress the third-order harmonic term component, thus providing a highly linear DC transfer characteristic with no extra power consumption. While fabricated in a 0.13 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, and supplied with <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ DD}}$ </tex-math></inline-formula> of 0.3 V, the circuit provides rail to rail input range with THD below 0.5 % for a nominal transconductance of 2.93 <inline-formula> <tex-math notation="LaTeX">$\mu \text{S}$ </tex-math></inline-formula>. The transconductance can be tuned from 1.41 <inline-formula> <tex-math notation="LaTeX">$\mu \text{S}$ </tex-math></inline-formula> to 5.72 <inline-formula> <tex-math notation="LaTeX">$\mu \text{S}$ </tex-math></inline-formula>, with rail-to-rail input swing and THD below 1%. Thanks to its compact structure, the circuit occupies only <inline-formula> <tex-math notation="LaTeX">$935~\mu \text{m}^{2}$ </tex-math></inline-formula>, with transconductance/power ratio of 5.75 <inline-formula> <tex-math notation="LaTeX">$\mu \text{S}/\mu \text{W}$ </tex-math></inline-formula>.
Research on Battery Safety Management and Protection Technology of Energy Storage Power Station

In recent years, the operation life of energy storage power station is increasing, and its safety problem has gradually become the focus of the industry. This paper expounds the core technology of safe and stable operation of energy storage power station from two aspects of battery safety management and safety protection, and looks forward to the development trend of safety technology of energy storage power station in the future.
Review on development and application of SOC key technologies for electric vehicle battery packs

State of Charge (SOC) is an important parameter of the power battery of pure electric vehicles. This paper first briefly introduces the SOC estimation methods, then introduces the research status at home and abroad according to the factors affecting SOC estimation, and then introduces the practical application of SOC key technologies in electric vehicles. Finally, the shortcomings of the current methods are discussed, and the future development direction is analyzed.
Accurate IpDFT-based Frequency Estimation Algorithm for the Unbalanced Three-phase Voltage Signal

It is difficult to precisely estimate frequency of the unbalanced signal since its unknown parameters make the spectrum different from the real-valued sinusoidal signal. Moreover, spectral leakage the unbalanced signal also brings large estimation errors when the acquired signal cycles become very small. In this paper, we proposed a two-point IpDFT algorithm for frequency estimation of the unbalanced/balanced three-phase voltage signal. The signal was decomposed into a real-valued sinusoidal signal, and then the system frequency was estimated through the spectrum equation. Both long- and short- range leakage was compensated to obtain precise frequency. Simulation results proved the effectiveness of our method, compared with other DFT methods.
Wind Speed Prediction Model for Multiple Sites Using Heterogeneous Spatio-Temporal Learning

Understanding and predicting wind speeds at both multiple sites and steps is significantly important for wind farm power generation prediction. To capture temporal and spatial dynamics inherent in wind speed data, a wind speed prediction model for multiple sites is proposed, which leverages fusion learning of heterogeneous convolutional channels to extract the spatio-temporal characteristics of regional winds. The correlation of wind speeds in different timing scales is diversely exploited and integrated. A three-dimensional convolutional channel is designed to extract the medium-longterm characteristics, and a depthwise separable temporal convolutional channel is designed to extract the fine-grained dynamic characteristics in the short term. Then, wind speeds at multiple sites can be predicted with a fusion strategy on the multi-scale features. The simulation results on Automated Surface Observing System (ASOS) dataset show that the proposed prediction method can achieve significantly higher prediction accuracy when compared with the state-of-the-art similar prediction methods.
Adaptive Neural Model Matching Control for Uncertain Immune Systems via H∞ Approaches

The problem of the robust neural network-based model matching control is considered for a large class of uncertain immune systems. In order to achieve the purpose of therapeutic enhancement, it is essential to deal simultaneously with the effects of plant uncertainties, time-varying perturbations, and continuing environmental pathogens. Neural network control algorithm, robust H∞ control theory and VSC technique are combined to construct the hybrid adaptive/robust tracking control scheme such that the controlled immune system achieves a satisfactory model matching control performance. An adaptive neural network system is constructed to learn the behavior of the immune system dynamics. Moreover, an algebraic Riccati-like inequality must be solved to achieve a desired H∞ control performance. Consequently, the robust control scheme developed here can be analytically computed and easily implemented. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Integrated Electricity-Gas System Optimal Dispatch Based on Deep Reinforcement Learning

Optimal dispatch plays a significant role in the efficient work of Integrated Electricity-Gas System (IEGS). This paper employs the Deep Reinforcement Learning (DRL) method to solve the IEGS optimal dispatch problem to improve the efficiency of decision-making. Mathematical modeling for the power network, gas network, and coupling equipment in the IEGS is first conducted. The objective function and constraints of the IEGS optimal dispatch model considering the economy and low carbon emission are proposed. Then, the Soft Actor-Critic algorithm (SAC) is selected to solve the optimal dispatch problem. A case on a modified IEGS combining IEEE 9-node with Gas 9-node is introduced to verify the effectiveness of DRL on the optimal dispatch problem.
Photovoltaic dissipation technology based on FMSS active distribution network

With the increasing penetration rate of distributed generation(DG) in distribution network, the existing distribution network structure, regulation and control means restrict the access of DG, and reduce its dissipation capacity. The flexible multi-state switching (FMSS) can optimize the power quality of the distribution network, enhance the flexibility and reliability of the system, and improve the feeder voltage distribution and power supply reliability. In this paper, the influencing factors of limiting photovoltaic consumption in traditional active distribution network are analyzed, and then based on the topology structure, mathematical model and working principle of FMSS, the control strategy is constructed to improve the dissipation capacity of distributed energy. The feasibility of the method is certified by establishing simulation on PSCAD simulation software.
Numerical Model for Prediction the Scour Depth around Two Pipelines in Tandem

Scour around submarine pipelines is one of the factors that affecting their stability which need to be considered. There have been several experimental investigations on scour depth. Although many studies have been conducted to investigate scour below a single pipeline, the effects of the spacing between two pipelines on scour characteristics are not properly understood. This study is focused to investigate the effects of pipeline diameters (D), gap ratio (G/D) between the pipelines, and the Reynolds Number (Re) on the scour depth. Simulations are conducted for two pipelines with diameters 0.075 m and 0.1 m, where the gap ratio is , , and . In case the pipeline diameter, , it is found that the relative scour depth increases with increasing the gap ratio, where and are upstream and downstream pipeline diameters, respectively. Meanwhile, when , the scour depth decreases with decreasing the gap ratio. For the gap, , the scour depth is smaller than at the gap and due to the disturbance of the vortex shedding around the upstream pipeline.
A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals

A trained neural network classifier is often used to detect cardiac problems by the classification of heart sound signals, also known as phonocardiogram (PCG) signals. The choice of an appropriate training optimization algorithm for such a classification problem, on the other hand, is still being debated. In this paper, we use the bidirectional long short-term memory (biLSTM) network for the classification of sequences of short-time features extracted from labelled PCG signals. The classification performance of four different trained biLSTM models is described in terms of three different optimization algorithms that are used to train the classifier. The elaborated results on testing PCG signals show that the biLSTM classifier performs better when trained with the stochastic gradient descent with momentum (SGDM) algorithm than when trained with the RMSprop (root mean squared propagation) optimizer or the adaptive moment (ADAM) optimization algorithm. Furthermore, this classification method outperforms a baseline method.
The Cost of Reinforcement Learning for Game Engines: The AZ-Hive Case-study

Although utilising computers to play board games has been a topic of research for many decades, the recent rapid developments in the field of reinforcement learning - like AlphaZero and variants - brought unprecedented progress in games such as chess and Go. However, the efficiency of this process remains unknown. In this work, we analyse the cost and efficiency of the AlphaZero approach when building a new game engine. Thus, we present our experience building AZ-Hive, an AlphaZero-based playing engine for the game of Hive. Using only the rules of the game and a quality of play assessment, AZ-Hive learns to play the game from scratch. Getting AZ-Hive up and running requires encoding the game in AlphaZero, i.e., capturing the board, the game state, the rules and the assessment of play-quality. And different encodings lead to significantly different AZ-Hive engines, with very different performance results. Thus, we propose a design space for configuring AZ-Hive, and demonstrate the costs and benefits of different configurations in this space. We find that different configurations lead to a less or more competitive playing-engine, but the training and evaluation for different such engines is prohibitively expensive. Moreover, no systematic, efficient exploration or pruning of the space is possible. In turn, an exhaustive exploration can easily take tens of training-years.
A Survey on Voice Assistant Security: Attacks and Countermeasures

Voice assistants (VA) have become prevalent on a wide range of personal devices such as smartphones and smart speakers. As companies build voice assistants with extra functionalities, attacks that trick a voice assistant into performing malicious behaviors can pose a significant threat to a user’s security, privacy, and even safety. However, the diverse attacks and stand-alone defenses in the literature often lack a systematic perspective, making it challenging for designers to properly identify, understand, and mitigate the security threats against voice assistants. To overcome this problem, this article provides a thorough survey of the attacks and countermeasures for voice assistants. We systematize a broad category of relevant but seemingly unrelated attacks by the vulnerable system components and attack methods, and categorize existing countermeasures based on the defensive strategies from a system designer’s perspective. To assist designers in planning defense based on their demands, we provide a qualitative comparison of existing countermeasures by the implementation cost, usability, and security and propose practical suggestions. We envision this work can help build more reliability into voice assistants and promote research in this fast-evolving area.
Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets

We analyze the inductive bias of gradient descent for weight normalized smooth homogeneous neural nets, when trained on exponential or cross-entropy loss. We analyse both standard weight normalization (SWN) and exponential weight normalization (EWN), and show that the gradient flow path with EWN is equivalent to gradient flow on standard networks with an adaptive learning rate. We extend these results to gradient descent, and establish asymptotic relations between weights and gradients for both SWN and EWN. We also show that EWN causes weights to be updated in a way that prefers asymptotic relative sparsity. For EWN, we provide a finite-time convergence rate of the loss with gradient flow and a tight asymptotic convergence rate with gradient descent. We demonstrate our results for SWN and EWN on synthetic data sets. Experimental results on simple datasets support our claim on sparse EWN solutions, even with SGD. This demonstrates its potential applications in learning neural networks amenable to pruning.
Prediction Modeling for Application-Specific Communication Architecture Design of Optical NoC

Multi-core systems-on-chip are becoming state-of-the-art. Therefore, there is a need for a fast and energy-efficient interconnect to take full advantage of the computational capabilities. Integration of silicon photonics with a traditional electrical interconnect in a Network-on-Chip (NoC) proposes a promising solution for overcoming the scalability issues of electrical interconnect. In this article, we derive and evaluate prediction modeling techniques for the design space exploration (DSE) of application-specific communication architectures for an Optical Network-on-Chip (ONoC). Our proposed model accurately predicts network packet latency, contention delay, and the static and dynamic energy consumption of the network. This work specifically addresses the challenge of accurately estimating performance metrics of the entire design space without having to perform time-consuming and computationally intensive exhaustive simulations. The proposed technique, based on machine learning (ML), can build accurate prediction models using only 10% to 50% (best case and worst case) of the entire design space. The accuracy, expressed as R2 (Coefficient of Determination) is 0.99901, 0.99967, 0.99996, and 0.99999 for network packet latency, contention delay, static energy consumption, and dynamic energy consumption, respectively, in six different benchmarks from the Splash-2 benchmark suite, chosen among 6 different machine learning prediction models.
Aggregation Modeling and Power Characteristics Analysis of Temperature Controlled Load Based on Improved State Space

As an important demand response resource, the air conditioning load can realize the rapid response of the system control signal. In turn, it plays an important role in peak-shaving or valley-filling and suppressing new energy fluctuations. It is of great significance for power grid dispatching to establish a model which can describe the characteristics of load group aggregation. In this paper, an accurate polymerization model is established by Monte Carlo simulation. Then, aiming at the problem of low precision of the existing state space aggregation model, an improved state space aggregation control model is proposed to effectively improve the simulation accuracy. The simulation results verify the feasibility of air conditioning load to provide reserve capacity and the validity of the improved state space model. Finally, the influence of different control strategies on the aggregated power is analyzed, and the phenomenon of “shock power” when the temperature setting value is changed is effectively solved by group control.
Occurrence of Ornithobacterium Rhinotracheale in Polish Turkey Flocks

Abstract Introduction Ornithobacterium rhinotracheale (ORT) causes significant economic losses to the poultry industry around the world. The bacterium often affects poultry as part of multiple infections causing very serious clinical signs that are usually not limited only to the respiratory system. This study’s main objective was the retrospective detection and identification of ORT in turkey flocks. Material and Methods ORT identification was performed in 6,225 samples taken from 133 different flocks between 2015 and 2020. Molecular methods were used, specifically real-time PCR and traditional PCR. We focused on partial 16S rRNA gene sequences of isolates, which were compared with sequences obtained from GenBank. The reaction products were analysed phylogenetically. Molecular methods indicating secondary infections was carried out, and the bacterial composition of the upper respiratory tract was 16S metasequenced for selected flocks to identify any other pathogens. Results The presence of ORT was detected in 30.83% of samples by real-time PCR and 28.57% by PCR. Phylogenetic analysis of the PCR products from the turkeys samples showed that their sequences resolved into two main genetic groups. Tests for the occurrence of secondary infections showed the presence of Mycoplasma gallisepticum and M. synoviae in some samples but the total absence of Bordetella avium. The upper respiratory tract in turkeys was dominated by two major phyla Firmicutes and Proteobacteria. At the genus level, the genera Ornithobacterium, Mycoplasma, Gallibacterium, Avibacterium, and Escherichia-Shigella were found which may include pathogenic bacteria that can cause clinical symptoms. Conclusion The results of the analysis of multiple infection carried out in flocks with respiratory signs are probably associated with outbreaks of ornithobacteriosis in turkey flocks in Poland.
Identification of Charging Behavior for Electric Bicycles based on Supervised Fisher Classifier

The abnormal charging behavior of electric bicycles may give rise to serious fire accidents. In order to avoid these risks, it is important to monitor the charging process of electric bicycles. Non-intrusive load monitoring (NILM) technology is proven to be an effective tool in this field. Existing studies mainly focus on using large amounts of transient data to learn the charging characteristics of electric bicycles, while transient data is not easily accessible considering smart meters installed at residential customer’s side mainly provide steady-state data for analysis. In light of this issue, we proposed a non-intrusive identification method of charging behavior for electric bicycles based on supervised fisher classifier. Firstly, the collected electrical data is preprocessed by filling in the missing values and deleting abnormal data. Afterwards, the supervised fisher classifier is utilized to classify the data and identify the charging behavior of electric bicycles. Simulation is carried out based on realistic data in MATLAB. Results show that the proposed method can accurately identify the charging behavior of electric bicycles.