Miracle-dz/UniFAR-FTUs huggingface dataset:
Dueling Double Q-learning based Real-time Energy Dispatch in Grid-connected Microgrids

This paper presents a real-time scheduling strategy based on deep reinforcement learning (DRL) algorithm aiming to realize economic dispatch of microgrid energy storage considering operational uncertainties. Making the scheduling decision of microgrid is a non-trivial task due to the random fluctuations of new energy power generation systems and loads. In order to solve this problem, the double deep Q-learning algorithm with the dueling structure is investigated to ensure the reliability of the microgrid while considering the real-time electricity prices. The agent is tested on the actual data and the results show that the proposed algorithm can get small operation cost of the microgrid in complex situations.
NewSpace SAR: Disruptive Concepts for Cost-Effective Earth Observation Missions

Synthetic aperture radar (SAR) is a key remote sensing technique for Earth observation. Future SAR missions will deliver weekly high-resolution images of our planet, thereby allowing quantification of several essential climate variables. While this is a huge step forward compared to current systems, some applications require even more frequent temporal sampling or simultaneous acquisitions from slightly different observation angles. NewSpace SAR denotes all groundbreaking solutions that enable frequent and enhanced SAR imaging at affordable costs. Besides the technological developments, e.g., mass-produced platforms for constellations of SAR satellites, application-driven SAR system design approaches play a fundamental role. Moreover, disruptive concepts based on formations of alternately-transmitting satellites, waveform or phase encoding and multi-focus-post processing, allow relaxing the design constraints, reducing complexity, size, and cost of the SAR instrument, and still retrieving the desired information from SAR data. These solutions will help spreading the on-going NewSpace revolution to SAR remote sensing and posing the basis for future Earth observation missions that will yield remarkable societal benefits.
Distributed Secure Sparse Modeling Based on Random Unitary Transform

Data analysis on edge/cloud computing systems is becoming more important. However, if the information being analyzed may leak personal identification, the owners tend to withhold or partially block the data to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful analysis. As a result, the desired analysis accuracy may not be achieved. To deal with this issue, several studies have examined encryption systems that support data analysis of encrypted data. Among the likely encryption systems, we focus on those based on the random unitary transform. This is because the random unitary transform offers lower computational complexity than other encryption approaches, and several signal processing algorithms have been proposed. However, analysis models for distributed encrypted data, have not been studied deeply enough. In this paper, we construct an analysis model for random unitary transform encrypted data by deriving a sparse model based on the elastic-net solution. The analytical model can derive the same elastic-net solution as that yielded by processing the original data (i.e. without encryption). The analytical model supports distributed encryption, where different parts of a data set are encrypted at different sites independently. Moreover, the proposed encryption model solves the problem that the security strength of random unitary transform decreases under certain conditions. Data aggregation after distributed encryption improves the accuracy of analyzing distributed privacy-sensitive information.
Concurrency Analysis of Go and Java

There has been tremendous progress in the past few decades towards developing applications that receive data and send data concurrently. In such a day and age, there is a requirement for a language that can perform optimally in such environments. Currently, the two most popular languages in that respect are Go and Java. In this paper, we look to analyze the concurrency features of Go and Java through a complete programming language performance analysis, looking at their compile time, run time, binary sizes and the language’s unique concurrency features. This is done by experimenting with the two languages using the matrix multiplication and PageRank algorithms. To the extent of our knowledge, this is the first work which used PageRank algorithm to analyse concurrency. Considering the results of this paper, application developers and researchers can hypothesize on an appropriate language to use for their concurrent programming activity.Results of this paper show that Go performs better for fewer number of computation but is soon taken over by Java as the number of computations drastically increase. This trend is shown to be the opposite when thread creation and management is considered where Java performs better with fewer computation but Go does better later on. Regarding concurrency features both Java with its Executor Service library and Go had their own advantages that made them better for specific applications.
Prediction of Remaining Useful Life of Proton Exchange Membrane Fuel Cell based on Wavelet-LSTM

Remaining useful life (RUL) prediction is a promising method for proton exchange membrane fuel cells (PEMFC) to save costs and expand applications. This paper proposes a model based on wavelet transform and long-short-term memory (LSTM), which uses a combination of neural networks and wavelet transform. First, the pre-processed voltage sequence of PEMFC data is wavelet decomposed, and LSTM prediction is performed on the decomposed sub-waveforms respectively, and the prediction result is wavelet reconstructed for voltage prediction. The results show that compared with the voltage prediction method using SVM, LSTM and BiLSTM, Wavelet-LSTM can perform more accurate prediction under different load conditions.
Product and Ratio of Product of Fisher-Snedecor ℱ Variates and Their Applications to Performance Evaluations of Wireless Communication Systems

This article considers the product, and the ratio of the product of Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> random variables (RVs), which can be used in modeling fading conditions that are encountered in realistic wireless transmission. To this end, exact analytical expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the product of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> statistically independent, but not necessarily identically distributed, Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> RVs. Capitalizing on these, exact analytical expressions are then derived for the outage probability, average channel capacity and average bit error probability over cascaded fading channels. Moreover, some important statistical metrics such as amount of fading, channel quality estimation index, kurtosis, and skewness are also provided, since they provide useful insights on the characteristics of the encountered fading conditions. In addition, with the aid of the central limit theorem, an approximation for the PDF of <inline-formula> <tex-math notation="LaTeX">$N*$ </tex-math></inline-formula>Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> RVs is proposed using a lognormal density, and its accuracy is quantified in terms of the resistor-average distance. Finally, novel expressions for the PDF and CDF of the <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-fold product ratio of Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> RVs are also derived. As a potential application of our new results, a spectrum sharing network is considered, for which exact analytical expressions for the outage probability, delay-limited capacity, and ergodic capacity are derived. For the cascaded fading scenario and the spectrum sharing network, numerical examples are provided to show the impact of different channel-related parameters, such as fading severity, shadowing, peak and average interference power on the system performance, which is rather useful in the design of conventional and emerging wireless communication systems. Monte-Carlo simulation results are provided to corroborate the presented mathematical analysis.
Enriching reflux and parallel equalization PSA process for concentrating trace components in air

The enrichment of useful trace components in air was carried out using a new two-bed pressure swing adsorption (PSA) process that utilized an enriching reflux (ER) cycle designed specifically for enriching the heavy component. Another new feature of this PSA process was the use of parallel equalization (PEQ), which not only saved recompression energy, but also favorably exchanged the axial concentration distribution between the two columns. These new PSA concepts were demonstrated by experimentally enriching Xe and CO2 in air using 13X MS zeolite. ER PSA substantially enriched both gases to values as high as 80 times the feed concentration with 90% recovery using a pressure ratio of only 12.5. This enrichment was far superior to that achieved with a conventional stripping reflux (SR) PSA cycle, which was limited thermodynamically to the pressure ratio and was typically less than half of it. A parametric study revealed the effects of the pressure ratio, half-cycle time, feed flow rate, flow rate ratio (enr.
Iterative Learning Control for Multiple Time-Delays Discrete Systems in Finite Frequency Domain

This paper developes iterative learning control scheme and the stability conditions for multiple time-delays discrete system. By formulating the problem over repetitive process form using 2D theory, sufficient stability conditions for multiple time-delays discrete system are developed along the trial, which guarantees the trial-to-trial error monotonic convergence. Moreover, the generalized Kalman-Yakubovich-Popov (KYP) lemma allows the iterative learning control scheme to develope stability conditions with LMI constraints and analyze in the finite frequency domain. A numerical simulation for multiple time-delays discrete system is given to verify the proposed method.
A High-Accuracy Hardware-Efficient Multiply–Accumulate (MAC) Unit Based on Dual-Mode Truncation Error Compensation for CNNs

This paper presents a multiply–accumulate (MAC) unit that enables a dual-mode truncation error compensation (TEC) scheme based on a fixed-width Booth multiplier (FWBM) for convolutional neural network (CNN) inference operations. The proposed tailored TEC schemes of Modes 1 and 2 can achieve high MAC accuracy for a general or rectified linear unit-based CNN model with general (Mode 1) or positive/zero (Mode 2) input patterns. By pre-calculating the pre-known CNN model coefficients, the proposed dual-mode TEC scheme can be realized using minimal partial product operations with high hardware efficiency using a software–hardware codesign approach. Further, a reconfigurable architecture of the resultant MAC unit is presented to realize the proposed dual-mode TEC scheme. By evaluating the accuracy for 9-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and 25-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> MAC operations (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> denotes the number of times MAC is performed), a MAC operation using the proposed TEC scheme can achieve the highest accuracy for Modes 1 and 2, relative to contrast samples that directly employ the FWBM with a conventional TEC function. The hardware performances of 9-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and 25-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> MAC units are also evaluated using the TSMC 40-nm standard cell library. Compared with the contrast TEC-enabled designs, the proposed MAC unit exhibits higher hardware efficiency in terms of area, delay, and power consumption and achieves a minimum reduction of more than 40% in both area-delay-error and power-delay-error products. Moreover, the resultant 9-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and 25-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> MAC units are verified using a system-on-chip field-programmable gate array platform to test a CNN model for handwritten digit classification.
Deep Learning Algorithm for Judicial Judgment Prediction Based on BERT

With the continuous development of artificial intelligence AI, machine learning and deep learning algorithms have been applied to more fields and scenarios, changing people’s work and lifestyle, which also brought new development and opportunities to the judicial field. Judicial case judgment is a very important work in the legal field, and it is an indispensable process for judges to determine the nature and punishment of criminals. Judgment prediction can be seen as a multi-label text classification problem. Based on a large number of text files of judicial cases, we use the recently very popular pre-training language model Bidirectional Encoder Representations from Transformers (BERT) to train word embedding of the case data, and combines deep learning model algorithms such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Pyramid CNN (DPCNN), Recurrent Convolutional Neural Networks (RCNN) to predict judgment in judicial cases. Experimental results demonstrate that compared with the 6 methods of baseline, the deep learning model based on the BERT word embedding achieves significant improvements that accuracy of prediction can be increased by 8%-10%, which can help judges and lawyers make more accurate judgments.
PSPNet-SLAM: A Semantic SLAM Detect Dynamic Object by Pyramid Scene Parsing Network

Simultaneous Localization and Mapping (SLAM) plays an important role in the computer vision and robotic field. The traditional SLAM framework adopts a strong static world assumption for convenience of analysis. It is very essential to know how to deal with the dynamic environment in the entire industry with widespread attention. Faced with these challenges, researchers consider introducing semantic information to collaboratively solve dynamic objects in the scene. So, in this paper, we proposed a PSPNet-SLAM: Pyramid Scene Parsing Network SLAM, which integrated the Semantic thread of pyramid structure and geometric threads of reverse ant colony search strategy into ORB-SLAM2. In the proposed system, a pyramid-structured PSPNet was used for semantic thread to segment dynamic objects in combination with context information. In the geometric thread, we proposed a OCMulti-View Geometry thread. On the one hand, the optimal error compensation homography matrix was designed to improve the accuracy of dynamic point detection. On the other hand, we came up with a reverse ant colony collection strategy to enhance the real-time performance of the system and reduce its time consumption during the detection of dynamic objects. We have evaluated our SLAM in public data sheets and real-time world and compared it with ORB-SLAM2, DynaSLAM. Many improvements have been achieved in this system including location accuracy in high-dynamic scenarios, which also outperformed the other four state-of-the-art SLAM systems coping with the dynamic environments. The real-time performance has been delivered, compared with the geometric thread of the excellent DynaSALM system.
Modeling of Energy Management Systems using Artificial Intelligence

Using Machine Learning techniques is possible to analyze larges amounts of data collected by Energy Management Systems, which can be used to, for example, predict the energy bill. This paper presents the employment of Machine Learning techniques to numerically model the electrical consumption of one building of Ministries’ Esplanade (headquarter of the Brazilian Executive Government), alongside with the applications of such models to Energy Management Systems. The Machine Learning model consists of an ensemble of 6 techniques, namely: Ridge Regression, Random Forest Regression, Extremely randomized trees, Gradient Tree Boosting, Support Vector Machine, and Artificial Neural Network. The 6 techniques were trained in parallel on historical data from 2018 and 2019 obtained from the PGEN project, which describe the building’s instant characteristics through 12 electrical and climatic variables, and the dataset is entirely available. The ensemble model predicts the building’s power consumption (averaged in a period of 10 minutes) with an error of MAE=9.32 KW (equivalent to 5.10% of the mean value of the power) and were listed four practical applications to the model.
Parameter Optimization of Load Frequency Active Disturbance Rejection Control Based on Improved Particle Swarm Optimization

In order to improve the control accuracy and response speed of the load frequency control(LFC) method, this paper studies the nonlinear active disturbance rejection control(ADRC) method of the LFC system, and adds the improved particle swarm optimization(PSO) to solve the problem that the ADRC has many parameters which make it difficult to be set. A design method of load frequency active disturbance rejection controller based on PSO is proposed. Firstly, based on the model of interconnected power system, the system state space and output equation are established. Then the basic principles of the ADRC and PSO are introduced. After that the design flow of the ADRC based on PSO is given and incorporated into the LFC method. Finally, the method is applied to the two-area interconnected power system considering nonlinear governor's dead zone(GDZ) and generation rate constraint(GRC) factors under load disturbance and parameter perturbation. In different cases, the comparison with other algorithms shows a good control effect, which confirms the effectiveness and superiority of the load frequency active disturbance rejection control parameter optimization method based on improved PSO.
A Methodology to Model Daily Charging Load in the EV Charging Stations Based on Monte Carlo Simulation

Electrical vehicle (EV) charging station is an important facility where EV owners can recharge their vehicles. With increasing penetration of EVs, charging loads in EV charging stations are increasing and consequently have dramatic impacts on the distribution systems. Under this condition, load modeling of the charging station is of great importance to relative research work such as evaluation of impacts of the integration of charging stations, planning for charging networks and so on. In this paper, random factors that can affect daily EV charging load are firstly analyzed in detail. Secondly, a Monte-Carlo-simulation based methodology is proposed to model daily charging load in the EV charging station with considerations of random factors. Another contribution is to design a threshold of failed recharge attempts in the Monte Carlo simulation. In simulation, this parameter can be adjusted according to the amounts of the EVs that are charged in the charging station during a day. Simulation results validate the methodology proposed in this paper.
Finite Impulse Response Filter Design using Grasshopper Optimization Algorithm and Implementation on FPGA

This paper establishes an efficient design style of Type-1 FIR filter using Grasshopper Optimization Algorithm (GOA) and its implementation on FPGA. GOA is a newly developed population-based meta-heuristic optimization algorithm motivated by the swarming activities of grasshoppers. GOA with its better problem-solving capability has revolutionized the contemporary era. Minimization of faults in the optimum response and the estimated response in digital filters are the main feature of meta-heuristic optimization algorithms. Therefore, this algorithm has been broadly accepted in various fields due to its high efficiency in solving problem sets. Further, to emphasis the usefulness of the suggested algorithm, the simulated outcomes have been compared with the results of the well-established algorithms such as Parks McClellan (PM) Algorithm and Sine Cosine Algorithm (SCA) and it has been found that Grasshopper Optimization Algorithm (GOA) outperform PM and SCA in terms of stop-band attenuation and pass-band ripple. Additionally, this paper also explains the hardware function of the concept being contemplated within the FPGA platform.
Target Distance Calculation Method Using Image Segmentation

Today we have made remarkable progress in the field of hardware and software. We can design many systems that provide high performance at low costs. Those systems replace humans working in places where death is likely to occur with robots. This paper proposes a system in which a robot moves to given GPS points, then searches for targets and calculates the distances. We used GPS sensors and robot-mounted sensors to move the robot to a given GPS point. When it reaches the point, it scans its surroundings and searches for humans as targets. It uses a stereo camera to take photos and the image segmentation to detect targets. The robot calculates the distance to the target and estimates the target’s GPS coordinates and returns it to the operator. The results showed the calculated distance from 50cm to 200cm with an error rate of 2-4%.
Application for Generating Images Using Generative-Adversarial Networks

Nowadays there is an increasing interest in the field of image generation using neural networks. Images created by such neural networks can find practical applications in various fields. The article describes the stages of creating an application for generating images in a predetermined art style using neural network technologies. The review of scientific literature and analogues is given. The topology of the used generative adversarial network is also given. The process of creating a dataset is described.
Thyroid Nodules Stratification Based on Orientation Characteristics Using Machine Learning Approach

Patients with thyroid nodules should undergo further assessment to define malignancy stratification. One of the assessments is conducted using ultrasound. There are ten characteristics of thyroid nodules in the ultrasound examination. One of the characteristics is orientation. The orientation characteristic has its perks as it can tell the malignancy state from the growth direction to the orientation category. This study develops an approach to determine the stratification of the thyroid nodules based on orientation characteristics. The proposed approach consists of pre-processing, segmentation, feature extraction, and classification by using a support vector machine. Nine geometrical and moment features are used to describe orientation characteristics. Furthermore, the features are trained to differentiate two classes, namely are parallel and non-parallel. The testing results achieve more than 0.98 for accuracy, sensitivity, and NPV, respectively, while specificity and PPV achieve a perfect score. Looking by the results, it can be concluded that the proposed method has a good performance in differentiating orientation characteristics into a parallel and non-parallel category.