Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Twin for a 10 MW Electrical Drive System for Future Electric Aircraft Applications

Reliable and high power density electric machine and inverter solutions are essential for a variety of applications. Digital twin for future electric aircraft applications can contribute to analyze the system behavior and the effects between the fast-switching inverter and the electric machine. Within this paper, the concept of a “Digital Twin for a 10 MW Electrical Drive System for Future Electric Aircraft Application” is presented. A digital twin platform is based on an outer-rotor permanent magnet synchronous machine and serval interleaved multi-level SiC-inverters, which allows the usage of high switching frequencies and a high DC link voltage of 1500 V. The combination of those components have advantages in future electric aircraft applications. Several multi-level inverter topologies have been investigated and a first system design for a 10.5 MW inverter system is presented. Industry 4.0 Applications in Electric Drives Production
Stochastic Optimization of Dispatchable Load Response and Punishment Price with Response Uncertainty

Dispatchable load (DL) participating in demand response (DR) will promote renewable energy consumption. But with limited regulation capability, the response of the DL is uncertain, which is undesirable to the market participators. A punishment mechanism for response deviation may be set. But a loose punishment is not effective, but severe punishment will reduce the enthusiasm of DL supplier. This paper takes the response deviation as random contributing factor. A stochastic optimization model of the DL clearing price and punishment price is proposed based on the relationship between response price and amount, punishment price and response deviation. With the expectation model, it is transformed to deterministic optimization model. Numerical results are provided to validate the proposed models.
Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection

The frequent outbreaks of agriculture pests have caused heavy losses in crop production. And the small size and high similarity of agricultural pests bring challenges to the prompt and accurate pest detection using imaging technologies. The key impetus of this paper is to achieve a good balance between efficiency and accuracy for pest detection on the basis of agricultural image data mining. This paper proposes Pest-YOLO which is a real-time agriculture pest detection method based on the improved convolutional neural network (CNN) and YOLOv4. First, a squeeze-and-excitation attention mechanism module is introduced to CNN for mining image data, extracting key features, and suppressing unrelated features. Then, a cross-stage multi-feature fusion method is designed to improve the structure of feature pyramid network and path aggregation network, thus enhancing the feature expressiveness of small targets like pests. Finally, our Pest-YOLO realizes end-to-end real-time pest detection with high accuracy based on improved CNN and YOLOv4. We evaluate the performance of our method on a typical large-scale pest dataset including 28k images and 24 classes. Experimental results demonstrate that our method outperforms the state-of-the-art solutions including Faster R-CNN and YOLO-based detectors, and achieves good performance with 71.6% mAP and 83.5% Recall. The proposed method is effective and applicable for accurate and real-time intelligent pest detection without expertise feature engineering.
Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference

With the development of mobile position techniques, sensing the citywide traffic information has been well recognized as a crucial task for various urban computing applications, such as intelligent transportation system, location-based recommendation, and user mobility modeling. With the consideration of high cost for sensor installment and maintenance, the traffic monitoring spatial coverage is often very limited in practical urban sensing scenarios. The goal of this paper is to perform the traffic inference over road segments which lack of (with very limited) historical traffic observations. Towards this end, we propose a temporal multi-view graph convolutional network for C itywide T raffic V olume I nference (CTVI) which jointly captures the spatial-temporal dependencies across different time intervals and geographical locations. In our CTVI framework, we design our attentive multi-view graph neural architecture based on our generated spatial and feature affinity graphs, to perform the cross-layer message passing with the preservation of road segment-wise topological context. In addition, we develop a temporal self-attention module to encode the evolving traffic patterns over time, which incorporates the time-wise relation contextual signals into the main embedding space. Furthermore, we propose a joint learning objective function that consists of an unsupervised random walk enhancement and a semi-supervised spatio-temporal volume constraint to guide the learning of road segment representations for citywide traffic volume inference. Evaluation results on real-world traffic datasets demonstrate the superiority of our proposed CTVI framework as compared to state-of-the-art baselines.
One Node at a Time: Node-Level Network Classification

Network classification aims to group networks (or graphs) into distinct categories based on their structure. We study the connection between classification of a network and of its constituent nodes, and whether nodes from networks in different groups are distinguishable based on structural node characteristics such as centrality and clustering coefficient. We demonstrate, using various network datasets and random network models, that a classifier can be trained to accurately predict the network category of a given node (without seeing the whole network), implying that complex networks display distinct structural patterns even at the node level. Finally, we discuss two applications of node-level network classification: (i) whole-network classification from small samples of nodes, and (ii) network bootstrapping.
Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering

Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart-meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.
SECAUCTEE: Securing Auction Smart Contracts using Trusted Execution Environments

Smart contracts running on blockchains have emerged as an indispensable mechanism to enhance trust, security, transparency and traceability of data shared among critical distributed applications. Unfortunately, a smart contract deployed on a blockchain by itself is usually inadequate in maintaining data security and privacy because the data are replicated to all the nodes on the network. There has been some recent work that tries to tackle this privacy leakage issue in smart contract execution by integrating blockchains with hardware supported trusted execution environments(TEEs). Although TEEs ensure privacy to some extent, the smart contract execution can still be compromised if the developed code does not use the TEEs' capabilities correctly. One important security issue for leveraging TEEs in practice is the memory access pattern disclosure. Even though the TEEs encrypt all the memory content during the program execution, the memory access sequence can be observed by the malicious operating system, and can be used to infer sensitive information such as “who submitted the second highest bid to the auction? ”. Hence, for enhanced security for TEE based applications, the memory access pattern leakage need to be addressed. Given these observations, an apparent question that comes to light is, how can we use TEEs correctly to enable efficient, privacy enhancing and secure applications? In this work, we address this challenge in the context of digital auctions. We develop a novel generic and secure framework that allow an auction smart contract to run inside secure enclaves over Intel SGX based TEEs on a blockchain. To our knowledge, this is the first work that provides access pattern leakage free TEE based secure auction smart contract deployment. We achieve this by implementing oblivious execution (i.e., no memory access pattern leakage) of both first price and second price sealed bid auctions as templates. Furthermore, we implement an end-to-end encryption service to keep the bids secure. Our empirical results and privacy analysis show that this architecture does not cause a significant impact to efficiency given the level of security achieved.
A Deep Learning Approach for Generating Soft Range Information from RF Data

Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning approach to generate accurate SRI from RF measurements. In particular, the proposed approach is implemented by a network with two neural modules and conducts the generation directly from raw data. Extensive experiments on a case study with two public datasets are conducted to quantify the efficiency in different indoor localization tasks. The results show that the proposed approach can generate highly accurate SRI, and significantly outperforms conventional techniques in both non-line-of-sight (NLOS) detection and ranging error mitigation.
A Digital Correction Method to Reduce the Nonlinearity of Terahertz Transmitters

Terahertz frequency ranging from 100 GHz to 10 THz is able to provide sufficient bandwidth and has gained much attention in the area of 6G wireless communications. However, due to the inherent nonlinearity of terahertz components and integrated modules, the current modulation schemes are usually limited to simple schemes with low spectrum efficiency, such as QPSK. To improve the transmission spectrum efficiency, in this paper, a digital correction method is proposed to alleviate the terahertz transmitter nonlinearity at the receiver side. In this method, a simplified nonlinearity correction model is first constructed at the symbol level and then trained by using some known transmitted symbols. Subsequently, the trained model is inserted into the receiver link to compensate for the nonlinear distortion of the transmitter to improve the quality of the received symbols. As a result, modulation schemes with higher spectrum efficiency, such as 64-QAM, can be adopted to improve the transmission speed within the limited bandwidth constrained by practical terahertz components. Simulations verify that the signal quality can be effectively improved in terms of the error vector magnitude (EVM).
A Model-Free Current Prediction Control with Runge-Kutta Algorithm for Grid-Connected Inverter

The conventional model predictive control (MPC) strategy produce current errors when the parameters of the inverter mismatch, which increases the Total Harmonic Distortion of grid current. To solve this problem, an improved method based on Runge-Kutta algorithm (RKA) is proposed. On the basis of the mathematical model of grid- connected inverter, current error of parameters mismatch of conventional MPC is analyzed. Moreover, Lagrange interpolation is performed to achieve the predictive current instead of the slope parameters of RKA, because the uncertain model with detailed parameters affects the prediction accuracy. Finally, simulation results verify the accuracy and effectiveness of the improved RKA approach.
Detection of Plant Leaf-based Diseases Using Machine Learning Approach

Agriculture productivity is increasing day-by-day based on recent advances and research growth in technology. Detection of plant leaf-based diseases and for improving the quality of plant leaf-based is very essential in agriculture. Detecting various plant leaf-based diseases with human sight, many laboratory-based approaches like polymerase chain reaction, decrease in food production, pest management, hyper spectral techniques are identified for detection of diseases but they are very high time consuming and high cost to the farmers. Identification of recent advanced techniques and various systematic models using Machine Learning (ML) approaches may increase the agriculture productivity. Researchers worked on modern approaches in ML algorithms for detection of leaf diseases for increasing the accuracy results. Every approach has its importance and is focused towards the direction of ML applications and is also based on issues faced by the farmers. In this research paper, detection of leaf-based diseases is analyzed using Support Vector Machine (SVM), Random Forest algorithms. The performance metrics like Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Disease affected area of the leaf by using Euclidian Distance method and Accuracy results are compared to benefit the farmers with less time, low cost and increase our agriculture productivity.
Multi-objective Optimal Placement of Three-Phase Step Voltage Regulator Considering Voltage Unbalance

The trend of energy decarbonization accelerates the transition towards decentralized power distribution systems. However, the growth of distributed generation and loads may increase voltage unbalance in distribution systems. Consequently, it is essential to plan and operate the systems considering voltage unbalance to avoid some problems caused by unacceptable voltage unbalance. This paper proposes an optimal placement method for three-phase individually controlled step voltage regulators ($3\phi \text{SVRs}$) considering voltage unbalance. A multi-objective bi-level optimization model is constructed to consider optimal placement and operation simultaneously. The optimal placement considers the trade-off between distribution losses and device costs, while the optimal operation minimizes the losses under the constraints of the voltage profile. Cooperative operation with a load ratio control transformer (LRT) is also incorporated in the optimization problem. The proposed method is tested using the IEEE 123 node test feeder. Results identify optimal placements for each number of devices and allow placement according to the preferences of distribution system operators.
Predictive Current Control Applied to Induction Machine Drive Systems Operating Under Single-Phase Open-Circuit Fault

This paper discusses the use of the Predictive Current Control (PCC) method in three fault-tolerant induction machine drive systems when they operate under single-phase open-circuit fault. Despite the fact that these drive systems have already been discussed in the literature, the reported papers always make use of either Field-Oriented-Control (FOC)- or Direct-Torque-Control (DTC)-based fault compensation strategies. In this way, performed simulations and experiments show that PCC-based strategies are feasible and are able to provide proper post-fault operation. Also, a comparison between FOC and PCC for all discussed drive systems is performed, showing that PCC provides much better transient response, with lower current and torque oscillations.
Meeting Demands for Mass Customization: A Hybrid Organic Computing Approach

The manufacturing landscape is experiencing a trend towards mass customization of products as customer demands continue to rise. Product life cycles are becoming shorter, while order fluctuations resulting from small lot-sizes increase. Traditional manufacturing lines, designed to produce high quantities of identical products, are ill-equipped to face the new challenges. While the paradigms are shifting, adoption of modern technologies for dynamic and flexible shop floor designs, such as cyber-physical systems and multi-agent systems, remains low. A clear pathway towards reconfiguring legacy systems stepwise is missing, and the new technologies are not yet established enough. We aim to alleviate these problems by introducing a novel, general simulation framework, developed with partners from the industry, for evaluation of manufacturing system designs. We highlight the usage of this framework by applying an online optimization model, utilizing techniques from organic computing, to optimize order and task prioritization in a hypothetical minimal modular smartphone production scenario. Our experiments show that the usage of an independent ob-server/controller structure is suited to solving these problems. The outlined scenario and optimization procedure can be extended to cover real-life manufacturing systems of much greater complexity within the provided framework.
Fair Decision-making Under Uncertainty

There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we study a longitudinal censored learning problem subject to fairness constraints, where we require that algorithmic decisions made do not affect certain individuals or social groups negatively in the presence of uncertainty on class label due to censorship. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world discriminated datasets with censorship demonstrate the practicality of our approach.
Static Graphs for Coding Productivity in OpenACC

The main contribution of this work is to increase the coding productivity for GPU programming by using the concept of Static Graphs. To do so, we have combined the new CUDA Graph API with the OpenACC programming model. We use as test cases a well-known and widely used problems in HPC and AI: the Particle Swarm Optimization. We complement the OpenACC functionality with the use of CUDA Graph, achieving accelerations of more than one order of magnitude, and a performance very close to a reference and optimized CUDA code. Finally, we propose a new specification to incorporate the concept of Static Graphs into the OpenACC specification.
Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets

Lithium-ion batteries are used in most battery powered devices. Today’s research on Lithium-ion batteries mainly focuses on better energy management strategies and predictive maintenance. In this paper, a new approach based on auto-encoders and long short-term memory neural networks applied to usage data (voltage, current, temperature) is used to make a State of Health prediction. Encouraging results are obtained when conducting tests on various battery ageing datasets published by Sandia National Laboratories, the Massachusetts Institute of Technology and NASA’s Prognostics Center of Excellence.
Coil Array Real-time Commutation Law for Magnetically Levitated Stage with Moving-coils

It is essential to decouple the multi-degrees-of-freedom dynamics for controlling a multi-degrees-of-freedom magnetically levitated stage with moving-coils.Decoupling the multi-degrees-of-freedom dynamics needs to decompose forces and torques acting on translator of the stage to their corresponding degrees-of-freedom.Force/torque decomposing can only be validated by real-time commutating the coil array because of the quasi-static magnetic field of the permanent magnet array.Firstly,the force/torque decomposing model is build by using the planar integration of Lorentz force law.Then,the coil array real-time commutation law of the multi-degrees-of-freedom magnetically levitated stage with moving-coils is established based on the force/torque decomposing model and the principle of minimal coil array heat-loss.Lastly,the numerical stability of the law and its error influence on corresponding force/torque are analyzed.Theoretical analysis and case research indicate that this law is of high numerical stability,it can calculate coil array currents accurately and reliably and the error of force/torque actuated by the law-commutated currents is quite small.This coil array real-time commutation law can be used to commutate the coil array,decompose the forces and torques,so as to control the multi-degrees-of-freedom magnetically levitated stage with moving-coils.
Target Detection With Unconstrained Linear Mixture Model and Hierarchical Denoising Autoencoder in Hyperspectral Imagery

Hyperspectral imagery with very high spectral resolution provides a new insight for subtle nuances identification of similar substances. However, hyperspectral target detection faces significant challenges of intraclass dissimilarity and interclass similarity due to the unavoidable interference caused by atmosphere, illumination, and sensor noise. In order to effectively alleviate these spectral inconsistencies, this paper proposes a novel target detection method without strict assumptions on data distribution based on an unconstrained linear mixture model and deep learning. Our proposed detector firstly reduces interference via a specifically designed deep-learning-based hierarchical denoising autoencoder, and then carries out accurate detection with a two-step subspace projection, aiming at background suppression and target enhancement. Additionally, to generate representative background and reliable target samples required in the detection procedure, an efficient spatial-spectral unified endmember extraction method has been developed. Performance comparison with several state-of-the-art detection methods and further analysis on four real-world hyperspectral images demonstrate the effectiveness and efficiency of our proposed target detector.