Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Clustering Algorithm of Clone Selection for Vehicle Image

Based on the clone selection theory in the natural immune system (NIS), this paper introduced a novel clone-based clustering algorithm which was shown to be effective at optimizing cluster rules to avoid trap of local minimum. We used the new algorithm to design the classifier of vehicle images, and it is shown to have better classification accuracy than other existing algorithms. First, the Density-based Spatial Clustering of Application with Noise (DBSCAN) algorithm was used to set up an initial antibody group to reduce the redundant clustering data. Then, combined with the clone selection principle, this article described the immune character of vehicle images. Through the proliferation and variation of antibodies, the affinities between antibodies and antigens were matured, and the antibody diversity was ensured. The global optimal solution can be achieved by utilizing this advanced algorithm, which also facilitates the identification of the finest partition for data sets. The experimental findings suggest that the new algorithm outperforms traditional clustering algorithms in terms of classification accuracy, and the new algorithm is effective and has practical value.
A Thevenin Model for Internal Electromagnetic Transient Characteristics Study of the MMCs

Modular multilevel converters (MMCs) are essential parts of power transmission systems. Studying internal transient processes in the case of system transient faults is required to enhance the insulating protection of the internal MMC devices, which necessitates modeling the detailed internal MMC structure. This study proposes a Thevenin equivalent model based on the associated discrete circuit (ADC) switching model (ADC-Thevenin) and then generates a parameter configuration method for the recommended model based on the principle of minimum energy. The suggested model addresses the shortcoming of the current Thevenin models, which ignore internal electromagnetic transient processes to speed up the calculation and is verified by the simulations of the single-terminal MMC system. Finally, the effect of stray parameters on the MMC impulse characteristics is examined using the ADC-Thevenin model.
Federated Temporal Learning Based Cyber Attack Detection for Distributed Industrial IoT Systems

Industrial Internet of Things (IIoT) is widely applied in modern industries. However, IIoT faces challenges in system modeling and cyber security. Distributed data isolation influences the time series data modeling, while frequent communications over IIoT leave vulnerabilities to cyber attacks. To ensure proper IIoT operations, we propose a federated temporal learning (FTL) mechanism to extract temporal features for cyber attack detection. We build local FTL models to accurately extract local time series features on edge devices. We collaboratively construct the global FTL model to solve the distributed heterogeneity at edges to further improve cyber attack detection. Moreover, we build a testing platform and emulate realistic cyber attacks towards IIoT. The proposed FTL detects seven types of cyber attacks more effectively compared with traditional models, in terms of comprehensive evaluation metrics. Thus, FTL is a promising IIoT security solution for distributed industrial systems.
Modeling and Simulation for Microgrids with Distributed Energy Resources of Low-Carbon Parks

Developing low-carbon parks is one of the promising ways to promote the green and low-carbon transformation of energy consumption. Microgrids (MGs), as one of the important components of low-carbon parks, have received increasing attention due to their capability to integrate the high penetration of distributed energy resources (DERs) and deal with their intermittent characteristics. With the increasing proportion of renewable energy and power electronic equipment, the modeling, dynamic feature analysis, and control strategy design of MGs is essential for their stable operation. Modeling is the foundation for stability analysis and controller design. However, the existing power system software, with some disadvantages such as difficulty in modeling and lack of flexibility, is unsuitable for the MGs characterized by a high proportion of renewable energy and power electronic equipment. This paper proposes a method to model the low-carbon park MGs with a structure retention model using MATLAB/Simulink. This method uses the custom module Matlab Fcn to build the low-carbon park MG power network model. Besides, dynamic elements, such as DERs, are modeled using the basic components based on differential-algebraic equations. Therefore, the proposed method can conveniently simulate the low-carbon park MGs with DERs and easily add modules and design controllers for the MGs. Finally, numerical simulations on an IEEE 34-bus distributed system in secondary frequency regulation demonstrate the validity and accuracy of the proposed method in the modeling and simulation for the low-carbon park MGs.
Anomalous Update Identification Based on Cosine Similarity for Collaborative Wind Power Forecasting

Wind power forecasting based on spatial correlation works in a collaborative way to exploit the information of neighboring wind farms to improve the overall forecasting accuracy, but the collaborative forecasting approach needs to centralize the original data of each site, which leads to the problem of data privacy preservation. The Federated Learning framework is applied to collaborative wind power forecasting, allowing the original data of each site to be stored locally for data privacy preservation. A method of identifying sites with anomalous update based on cosine similarity is proposed, based on which anomalous sites are eliminated in the subsequent collaborative forecasting process based on federated learning. A case study from 30 wind farms demonstrates that the proposed approach is effective to identify sites with anomalous update and achieves a forecasting accuracy consistent with the normal situation after eliminating the anomalous sites.
Fault Diagnosis Based on Multiscale Multivariate Dispersive Entropy and Bayesian-optimized LSTM Networks for Motor Bearing

Intelligent fault diagnosis of electric motor bearings can be achieved by analyzing their vibration signals. Deep learning methods have been widely used in fault diagnosis because of their ability to fit the behavior of complex systems better. However, the limitations of the single-channel time-series vibration signal and the randomness of the selection of hyperparameters for the deep learning model significantly impact the fault diagnosis results. This paper proposes a novel approach for fault diagnosis of motor bearings based on multiscale multivariate dispersive entropy (MMDE) and Bayesian-optimized long short-term memory (BOLSTM) networks. At first, MMDE is used to extract features from vibration signals of motor bearings, which are then fed into the LSTM networks for classification. Secondly, to optimize the performance of the LSTM networks, a Bayesian optimization algorithm is applied to find the optimal hyperparameters. Finally, the proposed method uses a bearing fault diagnosis dataset to validate the performance.
On Cauchy Problem for Semilinear Second-Order Parabolic Equations on Spheres

This article is devoted to solvability of the Cauchy problem for non-autonomous semilinear parabolic second-order partial differential equations on n-dimensional spheres. Such equations are non-homogeneous and non-autonomous analogs on spheres of the Kolmogorov–Petrovsky–Piskunov and Fischer equations.
ISD2K reference model to represent an information system oriented to knowledge production

The generation of knowledge (K) is a continuous and necessary effort for human evolution. Electronic records have enabled data processing, storage, and transfer to extend and accelerate this evolutionary effort. Electronic systems have been created to automate and record data from a process, for governments, organizations, and even for individuals. The expansion of these systems and the gradual increase of their complexity demanded the creation of Information Systems (IS) that aggregate data from several other systems to perform analysis and reports to support decision making, i.e., knowledge production (KP). The concept of Big Data - large volumes of data, with diverse structures, produced at an uncommon speed - describes the exponential growth of data production, but the same did not happen with the production of K. The objective of this work was to contribute to reduce this gap through the presentation of a reference model of IS to represent comprehensive and qualitatively measurable architectures in the KP. The method proposed in this work was the literature review about K, IS and reference models for information systems architecture (ISA), followed by the proposition of an IS reference model oriented to the conversion of data into knowledge (ISD2K) through a static and dynamic conceptual model. This proposition was validated from the description of a use case using ISD2K and elaboration of adherence questionnaires and evaluation of two IS in use at INPE (Instituto Nacional de Pesquisas Espaciais). The model is based on a common usage language, describing six views or perspectives and nine representations to each view that allow to deliver an integrated representation from different points of view inside an organization of an IS where is possible to distinct data, information and knowledge and plan and qualify the KP data oriented. The aspects of quality, utilization and impact were highlighted in this model as an innovative contribution to an IS model.
Discrete Resonant Controller based Current Harmonics Suppression for Dual Three-Phase Linear Induction Motor

In the electromagnetic propulsion system, the stator current harmonics of linear induction motor will worse control performance and increase loss. Wide range and fast change of frequency make it more difficult to suppress them. This paper proposes a discrete resonant controller based current harmonic suppression strategy. Through the pole correction and phase compensation, the problem of resonance point offset and phase lag caused by discretization and system delay is effectively solved, and the 5th and 7th current harmonics with frequency variation are greatly reduced without increasing the controller bandwidth. The hardware-in-the-loop experiments proved the feasibility and validity of the proposed controller.
Edge Assisted Mobile Semantic Visual SLAM

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts. In recent years, visual SLAM has been prevailing for autonomous driving. However, the ever-growing computation resources demanded by SLAM impede its applications to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation of edgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implement edgeSLAM and plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show that edgeSLAM can run on mobile devices at 35fps and achieve 5cm localization accuracy from real-world experiments, outperforming existing solutions by more than 15%. We also demonstrate the usability of edgeSLAM through 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge, edgeSLAM is the first edge-assisted real-time semantic visual SLAM for mobile devices.
Neural Network-Based Test Case Prioritization in Continuous Integration

In continuous integration environments, the execution of test cases is performed for every newly added feature or when a bug fix occurs. Therefore, regression testing is performed considering various testing strategies. The Test Case Prioritization (TCP) approach considers reordering test cases so that faults are found earlier with a minimum execution cost. The purpose of the paper is to investigate the impact of neural network-based classification models to assist in the prioritization of test cases. Three different models are employed with various features (duration, fault rate, cycles count, total runs count) and considering information at every 30 cycles or at every 100 cycles. The results obtained emphasize that the NEUTRON approach finds a better prioritization with respect to NAPFD (normalized average percent of the detected fault) than random permutation and is comparable with the solutions that used either duration or faults, considering that it combines both values. Compared to other existing approaches, NEUTRON obtains similar com-petitive results when considering a budget of 50% and the best results when considering budgets of 75% and 100%.
Partitioning Large-Scale, Multi-FPGA Applications for the Data Center

With the deployment of FPGAs in a data center, there is the opportunity to build large multi-FPGA applications. In this paper, we design a partitioner to address the problem of efficiently assigning the various tasks of a large multi-FPGA application to individual network-connected FPGAs according to constraints that consider resource usage, communication bandwidth and communication latency. By using simulated annealing, we can modify the cost function as new objectives and constraints are determined. We build on the Galapagos multi-FPGA platform by introducing a multi-die shell to extend Galapagos to more recent FPGA boards and design the partitioner to work on any collection of single- and multi-die FPGAs. Finally, We evaluate the new shell and partitioner using micro-benchmarks and analyze the partitioning of a real-world multi-FPGA application, a Transformer model.
Q-learning based maintenance decision of missile single component

As a precision strike weapon, the strategic position of missile equipment is increasing. Therefore, the reliability of the missile equipment has put forward higher requirements. However, the traditional periodic maintenance methods are increasingly unable to meet the maintenance and security needs of missile equipment, which has resulted in missile weapon systems facing low operational readiness and high security costs. In order to maintain the missile equipment in a good performance state for a long time, and reduce the investment in the maintenance cost of missile equipment during storage, a reinforcement learning-based method to optimize the maintenance decision of missile equipment is proposed. First, the future health state of the missile is predicted by the degradation pattern of missile components. Second, a Markov maintenance decision model of the missile is developed based on the degradation mechanism of missile components. Finally, with the maintenance cost of missile components during storage as the optimization objective, the Q-learning method is used to optimize the maintenance decision of missile components during storage. Through experimental simulations, the optimal maintenance cost of the missile single component—engine during storage and the optimal maintenance decision during storage are obtained.
Analysis of the Efficiency of Using Cascade-Frequency Control of a Group of Feed Pumps at an Industrial Power Plant

The paper considers the issue of increasing the energy efficiency of factory power plants. A review of the literature was carried out, in which Russian scientific works are presented. It was revealed that one of the main factors that reduce the energy efficiency of power plants is the consumption of electricity for own needs. In order to reduce the consumption of electricity by own needs, it is proposed to use the principle of cascade-frequency regulation to control a group of pumps. The feed pumps of the factory power plant with feed water cross-links were chosen as the object of study. An example of the operation of four feed pumps for a common collector with the condition of maintaining the required water flow at a given network pressure is considered. Instead of traditional throttling, it is proposed to control the performance of pumps by changing the speed of rotation of the drive. This reduces the consumption of electrical energy. A distinctive feature of the proposed control scheme is that some of the pumps required to maintain a given feed water flow rate are connected to one frequency converter. Regulation takes place at the same speed. A block diagram for selecting the optimal operating mode for a group of pumps of the same type has been developed. For a given network characteristic, various options for connecting pumps through frequency converters were obtained. The expediency of operation of a group of pumps with different frequency and load when working on a common network is considered. Recommendations have been developed for the use of frequency-controlled noses, taking into account the minimum costs for a given performance.
Trust Evaluation of Topological Nodes in Intelligent Connected Vehicles Communication Network under Zero-Trust Environment

This article examines the evaluation of trust in communication topology nodes within intelligent connected vehicles operating under a zero-trust environment. We present a trust model specifically designed for the zero-trust environment. Initially, we determine the comprehensive trust function of communication topology nodes in ICVs under the zero-trust environment by employing a weighted calculation of the node importance function and the node trust function. The trust function gradually diminishes over time. Next, we establish a communication topology model under the zero-trust environment, assess the consistency of the ICV queue, and investigate the impact of varying node trust levels on the entire communication topology. Finally, we utilize the two-predecessor following topology structure as an illustration to validate the effectiveness and feasibility of our proposed framework for evaluating the trust of communication topology nodes in ICVs within a zero-trust environment. This study serves as a valuable resource for enhancing the security of ICV communication.
Analysis of Low-Dropout Voltage Regulator designed with Gate-All-Around nanosheet transistors

In this work the Low Dropout Voltage Regulator (LDO) was designed with gate-all-around nanosheet transistors (GAA-NSH). The simulation model was developed using Verilog-A code and the Look Up Table (LUT) was based on experimental data. The gm/ID of 10.5 V−1 was used for the differential pair in the LDO circuit, resulting in a dropout voltage of 340 mV through the power output transistor. The LDO designed with NSH transistors demonstrated promising results, such as an open-loop gain of 57 dB, a gain-bandwidth product of 52 MHz, and a power rejection rate of approximately −70 dB.
A Method for Detecting the Reducers’ Grease Failure Based on Vibration Signal Analysis

Real time detection of the lubricating grease’s effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task.
On Using Electrical Impedance Measurements for Fish Detection in Sea- and Freshwater

Remote fish detection techniques allow to collect data of crucial importance from the point of view of sustainable water resources management. They can also drive development of selective fishing techniques, with minimal ecological impact. Here, we study the possibilities of using electrical impedance measurements for detecting fish in fresh- and seawater environments. We conducted experimental investigations and numerical simulations to determine changes in measured resistance and reactance values due to appearance of a fish in vicinity of electrodes. We used cameras and optical detection as a reference. Electrical impedance measurements allow for short-range remote fish detection, which might be suitable for specific real-life applications. In freshwater, we used parallel strip electrodes to count fish passes in a laboratory tank. The setup was a model of narrow fish passages, which are of great importance for monitoring fish populations e.g. in flowing waters or in aquacultural facilities. In seawater, we used a set of electrodes attached to a dielectric beam and swept over a bottom to detect flatfish partially buried in sand. The detection signal could potentially be used to trigger a startling stimuli in an improved, selective bottom trawling fishing gear. The results demonstrate that fish appearances in the detection regions are manifested by increase of the measured resistance values in freshwater and decrease in seawater. The corresponding reactance values decrease in the both cases. Numerical simulations performed for the freshwater environment using a simplified fish model and generalized electrical tissue characteristics are in fair agreement with the results of measurements, proving to be a valuable tool for supporting design and optimization of the impedance-based fish detection systems.
Remote Identification of Neural Network FPGA Accelerators by Power Fingerprints

Machine learning acceleration has become increasingly popular in recent years, with machine learning-as-a-service (MLaaS) scenarios offering convenient and efficient ways to access pre-trained neural network models on devices such as cloud FPGAs. However, the ease of access and use also raises concerns over model theft or misuse through model manipulation. To address these concerns, this paper proposes a method for identifying neural network models in MLaaS scenarios by their unique power consumption. Current fingerprinting methods for neural networks rely on input/output pairs or characteristic of the decision boundary, which might not always be accessible in more complex systems. Our proposed method utilizes unique power characteristics of the black-box neural network accelerator to extract a fingerprint by measuring the voltage fluctuations of the device when querying specially crafted inputs. We take advantage of the fact that the power consumption of the accelerator varies depending on the input being processed. For evaluation of our method we conduct 200 fingerprint extraction and matching experiments and the results confirm that the proposed method can distinguish between correct and incorrect models in 100% of the cases. Furthermore, we show that the fingerprint is robust to environmental and chip-to-chip variations.