Miracle-dz/UniFAR-FTUs huggingface dataset:
Agent-based Self-discipline Operation of Integrated Energy System

This paper presents a Self-discipline economic operation method of integrated energy system (IES) considering fault state based on agent. Firstly, various energy supply equipment in the IES are modeled, and the objective function of system economic operation is designed. Then, the Markov decision process is introduced, the economic operation problem of IES is transformed into Markov decision problem, and the agent model of optimal operation is established. Then the agent training based on historical running data is completed by using deep deterministic policy gradient (DDPG) algorithm. Finally, taking the IES of a multi-energy park as an example, the rationality of the output change of each equipment after agent decision under different fault conditions is analyzed.
Unsupervised Method for Wildfire Flame Segmentation and Detection

In the last decade, there have been many reports on the negative impact of wildfires on various ecosystems. Unfortunately, wildfires have been intensifying as global temperatures, droughts, and other instances of extreme weather events rise around the world. These circumstances are forcing communities to vigorously address the uncontrolled spread of wildfires, where the ultimate goal is the protection of wildlife. At the same time, many disaster prevention and monitoring methods, based on image processing and computer vision, have been developed. In this paper, we present a new unsupervised method based on RGB color space for the early detection of wildfires from still images. From the analysis of existing state-of-the-art methods, it is evident that different methods explore different color spaces for the extraction of flame features. Our motivation was to use only RGB color space and thus eliminate the time-consuming task of color space conversion. The proposed method consists of several new image processing techniques used to efficiently extract flame features. It outperforms the existing methods, where an increase of 3% and 2% is recorded in the F1 score and Matthews correlation coefficient, respectively. Such performance demonstrates the merits of the proposed method for flame segmentation and detection.
A Review on Character Recognition and Information Retrieval from Ancient Inscriptions

In today's digital age, there is a need to digitize ancient images and documents, the ancient periods from stone inscriptions reveals the cultural history and helps to preserve and emphasize our country's heritage A recognition system aids in the analysis and digitization of traditional cultures and heritages. In this case, digitization entails the recognition of ancient images in both online and offline modes. This paper discusses a variety of strategies and techniques for retrieving, classifying, and extracting information from ancient inscriptions in a variety of languages.
Design of Low Power PMOS Biased Sense Amplifier Using Lector Approach

A sensing amplifier is a component of a semiconductor memory chip's circuitry. The functionality, performance, and reliability of core memory architectures are all influenced by sense amplifiers. Two circuits are defined in this study. The suggested PMOS biased sensing amplifier is implemented in this circuit, which produces the same output results as prior work, but with a much higher decreased sense delay and power dissipation due to lower output impedance attributed to the use of lector approach in power gating strategies. As a conclusion, the simulation results accomplish the same functions as traditional circuits while consuming less energy. Using Tanner EDA and 180nm technology file, the total performance of the proposed sense amplifiers was simulated and analyzed.
Modelling of hydrogen production bioprocess using Artificial Neural Networks (ANN).

There is a renewed interest in biohydrogen production as a potential alternative to depleting fossil fuels. Its scale up requires the availability of accurate and reliable process models that relate the key operational parameters to hydrogen yields at various scales of the process development. In this paper, the Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to model and optimize biohydrogen production at two different process scales. The input variables consisted of inoculum size (10-50%), molasses concentration (100-300 g/L) and Hydraulic Retention Time (10-48 h) and the output was the hydrogen yield (mol H2/ mol sucrose consumed). The considered process scales were the culture volumes of 80 and 800 mL. Seventeen experimental data were generated at each scale and used for model development and process optimization, thus a total of two models at each scale. ANN based models gave R2 values of 0.99 and 0.95 whereas RSM based models gave R2 values of 0.97 and 0.89 for 80 and 800 mL, respectively. Process optimization with these models gave predicted yields of 0.87 and 0.73 mol H2/mol sucrose consumed (ANN based models) and 1.09 and 0.72 mol H2/mol sucrose consumed (RSM based models) for 80 and 800 mL, respectively. Models validation on ANN models gave experimental yields of 0.89 and 0.71 mol H2/mol sucrose consumed compared to 0.99 and 0.70 mol H2/mol sucrose consumed (RSM models) for 80 and 800 mL, respectively. These models showed relatively negligible deviations from their predicted values across scales. The RSM model at 80 mL (RSM_Model80) predicted the highest yield compared to the other three models. A comparative analysis of the prediction errors indicated that the ANN model at 80 mL (ANN_Model80) displayed a higher accuracy for prediction on unknown data. Semi-pilot scale (8 L) process assessments under optimized conditions showed negligible yield discrepancies from the predictive values of the models at lab scale. Microbial community analysis using Next Generation Sequencing (NGS) revealed the presence of presumptive hydrogen-producing microorganisms which were members within the genus Clostridia, Enterbacter and Klebsiella. These findings suggested that miniaturization of experiments for biohydrogen model development do not significantly impact on the model accuracy, thus reducing costs during the process developmental stage.
An Antibiotic Terahertz Spectrum Recognition Method Based on CNN and Attention-BiLSTM

The terahertz spectrum has the characteristics of a fingerprint spectrum, which can realize the identification of antibiotics. Antibiotic identification based on traditional learning methods has achieved certain results. Models based on deep learning can automatically extract kernel features, and convolutional neural network (CNN) models rely on a convolution layer to extract features. In view of the high characteristic dimension of antibiotic spectrum sequences, we propose a new CNN model and attention bidirectional long short-term memory (BiLSTM). We use CNN to reduce the dimension of an antibiotic sequence. However, the convolution kernel limits a CNN's long-term dependence in processing time-series signal data. BiLSTM can effectively solve this problem and capture the dependence before and after the timing signal. The attention mechanism is added to BiLSTM to further extract the subtle features of the antibiotic spectrum, and can better capture the most important local information. A full connection network achieves the purpose of antibiotic identification. In experiments, the F1 score of the proposed model is 0.98, confirming its strong recognition ability and good interpretability.
Receiver Synchronization of Ultra-Wideband Phase Modulated Signals with a Fully Analog QPSK Costas Loop

A fully analog QPSK Costas loop is verified by measurements and shows long term stability and locking for ultra-wideband signals with bit rates up to 2 Gbit/s of BPSK and QPSK modulated signals. The loop is used for receiver synchronization and tested with carrier frequencies up to 7.5 GHz. The effects of time delay on the locking behavior of the loop are briefly addressed in a theoretical manner and generally confirmed by measurements. To the best of the author's knowledge, this is the first successful QPSK measurement of a long-term stable locked, fully analog QPSK Costas loop.
Research and Application of Integrated Data Fusion Terminal for Station Area Operation and Distribution Based on Edge Calculation and Software Definition

In this paper, we studied the integrated data fusion terminal for distribution area operation and distribution based on edge calculation and software definition. Firstly, the overall architecture of the integrated data fusion terminal for distribution area operation and distribution was constructed, and the software and hardware of the terminal were comprehensively designed. Then, an edge data stream hierarchical storage algorithm and an edge computing resource allocation method based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm were proposed. In order to meet the capability of edge computing, the resource utilization efficiency, running time and throughput efficiency of CPU were compared and analyzed. The optimal CPU chip was selecited to complete the optimal design of terminal control system. Finally, combined with the field, the terminal was applied. The functions of data monitoring, fault early warning, topology identification and distribution area line loss were realized, which meets the professional needs of marketing and production, reduces the investment cost of power grid equipment and improves the on-site maintenance efficiency.
Aggregated Control of Smart Building Units for Frequency Regulation in Multi-Region Power Systems

The grid frequency regulation is a challenging problem in power systems with high renewable penetration. The smart building units (SBUs) consisting of thermostatically-controlled loads have the capability to participate the grid ancillary service considering the users’ comfort level. This paper presents a distributed aggregation control framework for multiple SBUs in a residential community, so that they can be utilized for grid frequency regulation. The proposed method is designed by using a finite-time distributed leader-follower consensus control. A leader control with two-control mode is proposed, which includes (i) frequency regulation mode (ii) comfort restoration mode. The leader control will determine the references of power and comfort state for the aggregated SBU. With the proposed method, the aggregated SBUs will be capable to track power reference and recover energy state in finite-time. The presented aggregation approach can effectively improve the frequency stability with consensus state sharing in SBUs. We have tested the capability of the developed control scheme in a three-region power system under different scenarios.
Edge-End Based Demand Response System of Aggregated Residential Load: Case Study

Residential load is widely distributed and the monomer capacity is small, but the proportion of total energy consumption on the occupied demand side has gradually increased recently. Meanwhile, the rapid development of Internet of things technology provides the possibility of fine regulation of residential load. In order to open up the refined and friendly interaction channel between distribution system operators and residents, this paper proposes an edge-end coordinated aggregated residential load demand response (DR) system. Firstly, the design framework, software, hardware and functions of the system are described, and then two applications of NILM and residential DR and their actual operation effects are introduced. The system realizes the localized storage and analysis of residential privacy data, and realizes the mining of residential DR potential.
A CMOS 24–30-GHz Low-Phase-Variation Variable Gain Amplifier Design for 5G New Radio

This letter presents a 24–30-GHz variable gain amplifier (VGA) achieving low-phase variation in a standard RF 65-nm CMOS process. The proposed VGA employs two stages. Phase compensation during the gain variation is realized with the dynamic complementary phase variation at the first stage. The second stage is utilized to increase the gain and keep linearity. Measurement results prove that the proposed VGA achieves a continuous gain tuning range of 8.3 dB. Under this condition, low-phase variation is realized with the root-mean-square (RMS) phase error less than 3° from 24 to 30 GHz. Moreover, the RMS phase error is less than 2° at the desired 5G frequency band from 27.5 to 29.5 GHz. The peak gain is measured with 18.7 dB at 29 GHz. With the max gain setting, the measured OP1dB and OIP3 are 5.1 and 9.3 dBm, respectively, and the measured noise figure is from 4.6 to 6.8 dB at the frequency range from 24 to 30 GHz.
Application of Gradient Boosting Algorithm for Predicting Equipment Failures

This article discusses the process of predicting failures of deep-pumping equipment in the oil industry. A procedure for predicting failures of oil well equipment is proposed, which is based on the application of a gradient boosting algorithm and includes three stages. At the first stage, a set of features necessary for the analysis is formed, as well as a sample of data. At the second stage of data preprocessing, the following are performed: processing of missing values; coding of categorical features; getting rid of outliers by the interquartile range method. At the third stage, the gradient boosting algorithm is applied to predict failures of deeppumping equipment. The peculiarity of the algorithm implementation consists in the proposed scheme of actions, which means sequential fixing first the number of decision trees, and then the learning rate, involving the analysis of the dependencies of the root-mean-square error on both the learning rate and the number of trees. The result of predicting failures of deep-pumping equipment of an oil well is presented and the results of experimental studies using the developed models on real data are described. The predicted value of the number of failures of the deep-pumping equipment of the oil well for the subsidiary of the company Rosneft Nyaganneftegaz was calculated. The proposed procedure for solving the problem of predicting equipment failures based on the application of the gradient boosting algorithm is advisable to use in the process of obtaining an engineering education both when studying special disciplines and when performing final qualifying works.
Security Protection Method for the Edge Equipment of Power Internet of Things based on Blockchain

With the construction of the power Internet, the safety and reliability of edge Equipment of power grid have been put forward higher requirements. This paper proposes a blockchain-based security protection method for edge Equipment of power Internet of Things. Based on Blockchain technology, Hash chain and distributed storage scheme are used to realize lightweight authentication method, and an identity authentication scheme for edge Equipment is proposed in line with the characteristics of security and stability control system. A hierarchical access control strategy based on multi-attributes is established by mining the relationship of attribute permissions among data access roles in power Internet. The experiment shows that the average operation time of the new equipment's network access and certification is in the order of ms, which effectively meets the high requirements of timeliness and security of the power system. The access control strategy can meet the requirements of multi-entity and multi-role access in the power Internet scenario, and can monitor and identify the behavior compliance of various access Equipment, which greatly improving the communication security and efficiency between power Equipment.
Using Blockchain Technology in MANETs Security

Many systems have recently begun to examine blockchain qualities in order to create cooperation enforcement methods. This paper provides a complete and extensive evaluation of work on multi-hop MANETs with blockchain-based trust control between nodes. We contextualize the snag of security in MANETs resulting from the lack of trust between the participating nodes. We present the blockchain concepts and discuss the limitation of the current blockchain in MANETs. We review the promising proposed ideas in the state-of-the-art based on research papers. Finally, we discuss and summarize strategies and challenges for further research.
Analysis of multi-scenario power supply and demand balance in Shandong power grid based on the new generation PSDB platform

Based on the new generation of PSDB platform, this paper proposes the power supply configuration scheme of Shandong power grid, which is used for the simulation calculation of power supply and demand balance production in Shandong power grid. The five power supply configuration schemes proposed in this paper, the power supply of scheme 1 (basic scheme) only considers projects that have been completed, are under construction, have been approved and have relatively clear plans; Scheme 2 to Scheme 5 are all based on the power supply configuration scheme of Scheme 1 to configure a guaranteed power supply to ensure the power supply of Shandong Power Grid. The simulation results of the power configuration of Scheme 3 to Scheme 5 show that the increase of the scale of new energy or the flexibility transformation of coal power basically does not affect the power balance at the moment when the Shandong grid gap is the largest, but it can improve the power supply at other times.
Debugging Debug Information With Neural Networks

The correctness of debug information included in optimized binaries has been the subject of recent attention by the research community. Indeed, it represents a practically important problem, as most of the software running in production is produced by an optimizing compiler. Current solutions rely on invariants, human-defined rules that embed the desired behavior, whose violation may indicate the presence of a bug. Although this approach proved to be effective in discovering several bugs, it is unable to identify bugs that do not trigger invariants. In this paper, we investigate the feasibility of using Deep Neural Networks (DNNs) to discover incorrect debug information. We trained a set of different models borrowed from the NLP community in an unsupervised way on a large dataset of debug traces and tested their performance on two novel datasets that we propose. Our results are positive and show that DNNs are capable of discovering bugs in both synthetic and real datasets. More interestingly, we performed a live analysis of our models by using them as bug detectors in a fuzzing system. We show that they were able to report 12 unknown bugs in the latest version of the widely used LLVM toolchain, 2 of which have been confirmed.
Gearbox fault diagnosis under fluctuating load conditions with independent angular re-sampling technique, continuous wavelet transform and multilayer perceptron neural network

Most research efforts in gearbox fault diagnosis thus far have focused on diagnosing gearbox faults under stationary conditions. Efforts in diagnosing gearbox faults under non-stationary conditions have mostly involved an analysis of gearbox vibration signals under the speed-up or run-down processes. This paper attempts to diagnose faults in a single stage spur gearbox under non stationary conditions arising from fluctuating loads at the output of gearbox. The vibration signal corresponding to each independent revolution is synchronized from the revolution point of view by converting into the angular domain. This is accomplished experimentally by a simple process referred to as the independent angular re-sampling (IAR) technique. The IAR technique is accomplished by employing a multiple pulse tachometer arrangement. Through the IAR process, non-stationary signals in the time domain are converted into quasi-stationary signals in the angular domain. The angular domain signals, each representing one revolution of the gearbox drive shaft, are then decomposed with continuous wavelet transform. Optimal scales are identified based on superior energy-Shannon's entropy ratio of continuous wavelet coefficients (CWCs). The classification accuracy of a multilayer perceptron neural network is compared when CWCs from all scales and when CWCs from the optimal scales are fed to the neural network.
Vision-based Navigation Solution for Autonomous Underwater Vehicles

Vehicle navigation is fully autonomous when the system is capable of planing its path and execute it without human intervention. This research aims at introducing an AI-based approach for visual navigation in underwater environments. To achieve this, several challenges have to be overtaken, such as segmenting the images to filter the floating clutter typical in underwater environments. First, an annotated dataset with pairs of input images and segmentation grounds truths is essential for training a state-of-the-art AI model. Second, choosing a model adequate for image segmentation and training it. Finally, evaluate if this methodology improves the accuracy of visual navigation and scene reconstruction algorithms, such as online and offline SLAM. This approach achieved state-of-the-art results on the segmentation task, with 93% pixel accuracy and 85% IoU. At last, it was concluded that using the segmentation masks produced by the fully convolutional network improves the results of using offline and online SLAM algorithms.
A W-Band Up-Conversion Mixer with Integrated LO Frequency Doublers in a 60 nm GaN Technology

A W-band up-conversion mixer with integrated frequency doublers is designed and manufactured in a 60 nm GaN-on-Si technology. The mixer is a single-balanced transconductance mixer. Via a 900 hybrid, the single-ended LO signal is split into I/Q signals fed separately to two frequency doublers, generating differential 2 ×LO signals. The circuit is characterized by on-chip probing. The measured lowest conversion loss is 6.4 dB. The 3-dB RF bandwidth is 8 GHz from 87.5 to 95.5 GHz and the 3-dB IF bandwidth is 4.0 GHz from 0.1 to 4.1 GHz. The output referred P1dB is -8.5 dBm at 93 GHz RF frequency. The input power (LO) of the doubler is 17 dBm. The total DC power consumption of the mixer and the doublers is 620 mW. To the best of authors’ knowledge, the mixer achieves the lowest conversion loss among the E/W-band GaN mixers published in the literature.
Impact of 5G base station participating in grid interaction

Under the background of the gradual development of 5G network , the number of 5G base stations grows exponentially , resulting in the problem of high energy consumption of 5G base stations. How to build a green and energy-saving 5G network and reduce the electricity purchase cost of communication operators has become a key issue. This paper summarizes the communication characteristics and energy consumption characteristics of 5G base stations based on domestic and foreign literature , and studies the potential of 5G base stations to participate in the power grid demand response. Finally , a further prospect is proposed on the research status of 5G base station's participation in power grid interaction.