Miracle-dz/UniFAR-FTUs huggingface dataset:
AI Approach for FRA Based Condition Assessment of Power Transformers

Condition assessment of power transformers are important to avoid power system failures because it usually cause significant economic losses. In order to detect the mechanical integrity of power transformer windings, Frequency response measurements (FRA) has been developed. It is an effective tool to detect the internal mechanical integrity. However, interpretation of FRA results can sometimes be tedious when comparing transformers with different structures. This paper presents a FRA measurements based AI approach to recognize different faulty and non-faulty transformers irrespectively of their design structures. FRA data sets were collected from 10, Field aged power transformers with different capacities and each data set consisted of 100 features i.e., 4 FRA frequency bands, 5 FRA measuring methods and 5 statistical analyzing parameters. It was found that with two Principal Components, faulty and non faulty transformers could be easily separated. Accordingly, the 100 features were reduced to 64 features by optimizing FRA measuring methods from 5 to 3. Then different ML techniques and NN algorithms were used to further classify the faulty transformers into three different fault types. It was found that fine kNN was the best method for transformer classification with a 100% accuracy of the tested samples.
Gulf of Maine intermediate water

The thermohaline dynamics of the Gulf of Maine are analyzed from the two year, eight cruise, data set of Colton, Marak, Nickerson, and Stoddard (1968). Six water masses are described: the Maine Surface Water, Maine Intermediate Water, and the Maine Bottom Water as interior water masses; and the Scotian Shelf Water, the Slope Water, and the Georges Bank Water as exterior water masses. Particular attention is given to the formation and disposition of the Maine Intermediate Water. Salt balance, T-S volume, and T-S drift analyses are used to provide transport and mixing estimates for the year 1966. The Slope Water entered at depth through the Northeast Channel at a rate of 2600 km/sup 3//yr; while the Scotian Shelf Water entered the surface and intermediate layers, mostly during winter intrusions, at a rate of 5200 km/sup 3//yr. The surface and intermediate layers exported a total of 7900 km/sup 3//yr in a 3:5 ratio, respectively. The Maine Intermediate Water tends to collect over the Wilkinson Basin during the stratified season, to exit via the Great South Channel during early spring, and to exit via the Northeast Channel during spring and summer. Comparisons are made between the estimated winter heat loss of 280more » Ly/d and the observed heat losses of 230 Ly/d (surface layers) and 360 Ly/d (surface and intermediate layers). A limit for the Scotian Shelf Water contribution is about -70 Ly/d. It is concluded that the Maine Intermediate Water is produced locally and that it is exported in significant quantities. « less
Hardware Trojans in fdSOI

With shortening turn-around times and increasing complexity for digital circuits, design reuse, third party IP and today even physical chiplets has increased. Malicious actors have more options to introduce hardware backdoors to packaged systems, which will leak data if triggered. In this work, we show two novel approaches to introduce such backdoors, that are possible due to the specifics of fully depleted silicon on insulator (fdSOI) technology. The first method relies on modifying the doping profile of an antenna cell to introduce a covert short between the back gate and logic signals. The second method constructs specific illegal states which are latched when the clock is running with the trigger frequency. Basic test structures have been designed such that they are DRC and STA clean. LVS does not reveal the hidden structure, while measurements in silicon confirm their operation.
Dynamic Weapon Target Assignment Based on Deep Q Network

With the development of intelligent and unmanned warfare, the battlefield environment in the future is becoming increasingly complex, and the field of weapon target allocation has shown the characteristics of multiple styles, fast pace and strong uncertainty. Since the traditional exact algorithm and intelligent optimization algorithm cannot solve the dynamic weapon target allocation problem quickly and efficiently, this paper proposes a dynamic weapon target assignment (DWTA) based on deep Q network (DQN). Based on reinforcement learning, the neural network of deep learning is used for convergence and prediction, and the optimal strategy under the current conditions is obtained through continuous training between the target and the environment, so as to output the optimal interception scheme. In this paper, the simulation environment of weapon target assignment is constructed by Pycharm software and Tkinter model. The results of the assignment show that DQN algorithm is more than 50% faster and more accurate than the Q-learning algorithm. Finally, the proposed algorithm provides a new thinking for DWTA.
City-Scale Reality Modeling from Community Photo Collection

Many applications in Augmented Reality (AR) require the use of Reality Models 3D information tailored for use in AR systems. Available virtual information obtained from CAD models and GIS data is not always trustworthy for registration in AR, as it often has not been verified and might not accurately represent reality. Nowadays, images of landmarks, available on the Internet, allow for automatic 3D modeling, and we propose to deploy these datasets to model the reality. This paper is the first to use city scale models reconstructed from Internet photo collection in an AR framework. Our method augments the information of the generated Reality Models, which are typically represented through texture images and 3D geometry (sparse or dense). We automatically annotate the 3D models by analyzing the rich information contained in user provided tags of the original images. Additionally, we introduce an automatic procedure to align new uncalibrated images to our reality model, thus providing a method for the transfer of augmentation from the 3D-model to an image.
Dynamic Event-Triggered Consensus on Matrix-Weighted Networks: Leadless Case

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results.
Online 3D Reconstruction Based On Lidar Point Cloud

This paper aims to propose an online 3D mesh reconstruction algorithm based on lidar sensors for large-scale scenes. In comparison to point clouds, meshes provide stable geometry and richer semantic information. To address the issues related to sparse point clouds, inaccurate normal estimation, and slow normal calculation, we introduce a new 3D reconstruction algorithm system. Our approach combines the robot's motion with the mesh reconstruction method, enabling us to generate high-quality meshes from the lidar point cloud in a frame-by-frame manner and then stitch them together to form a global mesh. To evaluate the proposed algorithm, we test it on two different datasets - the Kitti dataset obtained using the 64-line Velodyne lidar and the office park environment dataset obtained using the 32-line Ouster lidar. Our experimental results show that the algorithm is not only fast but also has a certain level of robustness towards the density of the laser point cloud.
Convolutional neural network-based signal discrimination for P300 brain-computer interface

The P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. This paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. The test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects.
Automatic Image Screening of Pine Wilt Disease Based on TransUNet

Pine nematode disease, also named Pine wilt disease (PWD), has become a major disaster for the global forestry, devastating a large number of pine forests in the world and leading to great economic losses. In this paper, the small-sample forest image data is obtained through forest fire surveillance cameras and pre-processed by some augmentation methods. Then an automatic screening method for PWD based on the TransUNet network is proposed. This paper uses a small amount of image data captured by forest fire surveillance cameras instead of unmanned aerial vehicle (UAV) images. We perform pre-processing operations on the datasets, such as haze removal, clipping, and image filling. Multiple loss functions were selected, and a new loss function based on precision and recall named PR loss function, is developed in this paper. The results demonstrate that the proposed model has relatively high accuracy, with precision, recall, and F1 score of about 0.870, 0.756, and 0.807. Compared with many spectrum analysis methods and deep learning models, our proposed method can obtain better performance under small-sample and low-resolution restrictions.
Validation of Satellite Derived Snow Physical Parameters at Saroma Lagoon, Japan (Extended Abstract)

MASAHIRO HORI1, TERUO AOKI2, HIROSHI ISHIMOTO2, TOMONORI TANIKAWA3, KAZUHIRO NAOKI4, KENICHI MATSUOKA5, YASUKO OGURA6, AKIHIRO HACHIKUS07, RUNE STORVOLD8, HANS EIDE9, KNUT STAMNES5, BINGQUAN CHEN9 and WEI Le 'National Space Development Agency of Japan ; Harumi , Chuo-ku, Tokyo 104-6023 JAPAN 'Meteorological Research Institute ; Nagamine , Tsukuba, Ibaraki 305-0052 JAPAN, 3University of Tsukuba ; Tennodai , Tsukuba, Ibaraki 305-8577 JAPAN 'Hokkaido Univ . of Education ; Shiroyama, Kushiro, Hokkaido 085-8580 JAPAN 'University of Washington ; Seattle , WA 98195-1310 USA 6Graduate University for Advance Study ; Kaga , Itahashi-ku,Tokyo 173-8515 JAPAN 7Kitami Institute of Technology ; Koen-cho Kitami , Hokkaido 090-8507 JAPAN 'University of Alaska Fairbanks ; Fairbanks , AK 99775-7320 USA 9Stevens Institute of Technology ; Hoboken , NJ 07030 USA
Minerva Access is the Institutional Repository of The University of Melbourne

38 While population foraging behaviour of herbivores has been extensively studied, individual 39 choice is still poorly understood. Very few studies have focused on the individual consistency of 40 foraging behaviour in marine herbivores. Because marine ectotherms are strongly influenced by 41 their environment and because a mixed diet is appropriate for herbivores, we hypothesized that 42 Haliotis tuberculata, a large marine gastropod, would not exhibit significant individual 43 consistency in foraging activity and would display generalist food choices. To test these 44 hypotheses, the behaviour of 120 abalone was studied using a choice test of eight macroalgal 45 species over 3-weeks, with video recording 24 hours a day. In addition, primary components, 46 secondary metabolites and toughness of the eight algae were measured. At the population level, 47 food choice was mainly related to the protein composition and the toughness of the macroalgae. 48 We found that H. tuberculata is a generalist species feeding on a variety of algae (IS = 0.64), even 49 if 21% of the individuals can be considered to be specialists. However, in contrast to our 50 hypothesis, highly consistent between-individual variation was observed in foraging activity (ICC
Mitigating Circulating Currents of Parallel Three-Phase Vienna Rectifiers With Unbalanced Filter Inductors

Parallel connections of three-phase Vienna rectifiers are commonly used to increase the power ratings of electric vehicle charging stations in recent years. However, parallel operation can create a path for zero sequence circulating current (ZSCC), particularly when the rectifiers are operating under interleaved modes with unbalanced filter inductors. To tackle out this issue, an integrated control scheme is proposed in this article. Based on these frequency components of the ZSCC, the proposed control scheme has two layers. The low frequency component of the ZSCC is mitigated by using PIR control in the inner current control loop under the rotational reference frame, while the high frequency component of the ZSCC is reduced by applying the Discontinuous Pulse Width Modulation (DPWM) technique with proper zero sequence injections. The effectiveness of the proposed control scheme is validated through both numerical simulations and hardware experiments.
Velocity-free Formation Control for Quadrotors Using High-order Sliding Mode Differentiator

This paper deals with the problem of achieving formation control for underactuated multiple quadrotors without velocity measurements by employing the higher-order sliding mode (HOSM) differentiator. The primary objective of this work is to attain a coordinated formation behavior among the quadrotor group. In this paper, the dynamics of each quadrotor are transformed into a new form, wherein the attitude is described using rotation matrices. Subsequently, an intermediate guide is designed using the HOSM differentiator, taking into account the desired relative positions between the quadrotors and their actual positions. Next, the actual control thrusts and torques for each quadrotor are designed and solved by the intermediate guide. Finally, rigorous theoretical analysis and multiple simulations are performed to ensure that the multiple quadrotors converge asymptotically and accurately to the desired formation shape.
Time-attenuating Twin Delayed DDPG for Quadrotor Tracking Control

Continuous trajectory tracking control of quadrotors is challenging when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking accuracy and response time. We propose a time-attenuating twin delayed DDPG, a model-free algorithm that is robust to noise, to better handle the trajectory tracking task. A deep reinforcement learning framework is constructed, where a time decay strategy is designed to avoid trapping into local optima. The experimental results show that the tracking error is significantly small, and the operation time is one-tenth of that of a traditional algorithm. The OpenAI Mujoco[1] tool is used to verify the proposed algorithm, and the simulation results show that, the proposed method can significantly improve the training efficiency and effectively improve the accuracy and convergence stability.
NPC1L1 inhibition disturbs lipid trafficking and induces large lipid droplet formation in intestinal absorptive epithelial cells

Ezetimibe inhibits Niemann-Pick C1-like 1 (NPC1L1) protein, which mediates intracellular cholesterol trafficking from the brush border membrane to the endoplasmic reticulum, where chylomicron assembly takes place in enterocytes or in the intestinal absorptive epithelial cells. Cholesterol is a minor lipid component of chylomicrons; however, whether or not a shortage of cholesterol attenuates chylomicron assembly is unknown. The aim of this study was to examine the effect of NPC1L1 inhibition on trans-epithelial lipid transport, and chylomicron assembly and secretion in enterocytes. Caco-2 cells, an absorptive epithelial model, grown onto culture inserts were given lipid micelles from the apical side, and chylomicron-like triacylglycerol-rich lipoprotein secreted basolaterally were analyzed after a 24-h incubation period in the presence of ezetimibe up to 50 μM. The secretion of lipoprotein and apolipoprotein B48 were reduced by adding ezetimibe (30%, p <0.01 and 34%, p <0.05, respectively). Additionally, ezetimibe accelerated intracellular apoB protein degradation by approximately 2.8-fold and activated sterol regulatory element binding protein 2 by approximately 1.5-fold: These are indicators whether the cells are sensing cellular cholesterol shortage. Thus, ezetimibe appeared to limit cellular cholesterol mobilization required for lipoprotein assembly. In such conditions, large lipid droplet formation in Caco-2 cells and the enterocytes in mice were induced, implying that unprocessed triglyceride was sheltered in these compartments. Although ezetimibe did not reduce the post-prandial lipid surge appreciably in triolein-infused mice, the results of the present study indicated that NPC1L1-mediated supply chylomicron with cholesterol may participate in a novel regulatory mechanism for the efficient chylomicron assembly and secretion.
Dual Converter Operating With Floating Capacitors Connecting Open-End Winding Doubly-Fed Induction Generator to the DC Microgrid

This article presents a novel configuration to connect a three-phase doubly-fed induction generator (DFIG) to the dc microgrid. The proposed topology connects an uncontrolled converter and a full-controlled converter in series with the generator stator windings in an open-end winding (OEW) configuration. The full-controlled converter is connected to an independent dc link based on floating capacitors. The article discusses the converter operating principles, pulse-width modulation (PWM), DFIG power control, and the floating capacitors' voltage regulation. The work compares the proposed and conventional systems regarding current harmonic distortions, torque ripple, and semiconductor losses. Simulations and experiments show steady-state and transient results for a 0.56-kW DFIG operating at the rated power. Finally, it was verified that the proposed topology presents lower power losses and low-distorted voltages and currents over the DFIG stator windings.
Research on Transmission line alternatives selection based on cross-system data fusion

The safety of transmission lines is measured from several different perspectives. The data of different video surveillance, UAV monitoring, Blockchain social inspection, and internal and external network manual inspection of the transmission line are all connected to the display screen of the transmission line monitoring center. Then the health level of the transmission line is evaluated based on the different perspectives of data in real time. The evaluation needs a series of standard systems to evaluate comprehensively, the data of a single system is not accurate enough. In this paper, the fuzzy multi-attribute decision-making method is used to evaluate the optimal selection of data for each subsystem, and the optimal alternative is selected during the transmission line comprehensive, The method of mathematics can evaluate the health level of transmission lines more accurately, the results hope to provide some proposals for the operation of transmission lines to develop transmission line maintenance policies.
F-GAN: A fusion algorithm for surface defect detection based on generative adversarial network

With the continuous development of manufacturing intelligence, the focus of industrial research has gradually shifted from quantity to quality. There have been technical difficulties in the detection of surface defects of products. Therefore, the detection accuracy of product quality has become a research hotspot. In this paper, we propose a fusion algorithm F-GAN for unsupervised surface defect detection based on generative adversarial network. The method builds a network model based on encoder-decoder-encoder and incorporates an attention feature fusion module in the decoder, which can complete defect detection under defect-free sample conditions. In addition, we use structural similarity in the loss function to measure the reconstruction effect of the network from multiple perspectives. Finally, to visualize the defect segmentation effect of the network, we designed the residual binarization module to binarize the residual images to obtain the final defect detection results. We conducted experiments on the MVTec public dataset. The results show that our proposed method has a high level of AUC with an average improvement of 2.1%. In addition, to illustrate the effectiveness of the F-GAN, we conducted ablation experiments for different modules.
LAXOR: A Bit-Accurate BNN Accelerator with Latch-XOR Logic for Local Computing

Binary Neural Network (BNN) accelerators are attractive solutions for Artificial Internet-of-Things (AIoT) applications thanks to the compact models and low computational cost while maintaining satisfactory classification performance. Various analog/mix-signal compute-in-memory macros have been proposed to boost the energy efficiency of binary convolution tasks. However, this approach incurs inaccurate computation due to its sensitivity to temperature, noise, and process variations. In this work, we present a full-digital BNN architecture that leverages a novel Latch-XOR logic array for local bitwise multiplication, suppressing massive data movement and achieving 4.2× lower energy per operation compared to the decoupled standard cell approach. An optimized population count circuitry is also proposed for data accumulation, which obtains 1.37× Energy-Delay-Area saving compared to Binary-Adder-Tree-based implementation. To enable seamless hardware-software co-optimization, we have developed an in-house simulator for design space exploration as well as flexible mapping with various network topologies and kernel sizes. Our experiment shows the Latch-XOR-based architecture in 28nm CMOS technology achieves an enhanced energy efficiency of 2315 TOPS/W, 3.4× higher compared to the state-of-the-art synthesized digital architecture. This manifests that the proposed accelerator is highly suited for AIoT applications.
A Validation Study of the Multicultural Counseling Inventory for School Counselors

This article summarizes the exploratory factor analysis and confirmatory factor analysis results from a sample of ( N = 322) school counselors’ multicultural competence in three Midwestern states. Findings indicate the factor analytic structure of the original Multicultural Counseling Inventory (MCI) can be revised to a parsimonious 15-item, three-factor model for school counselors. Implications suggest that research with the refined 15-item MCI in other areas and more diverse populations is warranted.
A Teaching Suggestion for Active Disturbance Rejection Control

Recently, more and more engineers and students want to learn ADRC, and teaching ADRC in the framework of mainstream control theory becomes an urgent task. This note suggests a route on teaching ADRC. It starts with the extended estimator of [1], [2], then introduces second-order linear ADRC, then generalize to n-th order linear ADRC, finally discusses the relationship between second-order linear ADRC and PID control. This route is consistent with the main stream of control theory, pays attention on both time-domain and frequency-domain, and reflects the historical progress of ADRC.