Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Robust Stereo Visual SLAM System Based on Bionic Eyes

Visual Simultaneously Localization and Mapping (SLAM) systems obtain accurate ego-motion of cameras and 3D representation of the environment. Although the performance of SLAM systems is very impressive in many common scenes, tracking failure is still a very challenging issue, which always exist in low-textured environment and rapid camera motion situations. In this paper, we propose a real-time stereo visual SLAM system using bionic eyes. The new type sensor named bionic eyes are basically the stereo cameras and perform all the movements of human eyes. Our system makes a good combination of sensors and algorithms. In order to find more feature points, we proposed an autonomous searching strategy of the bionic eyes, which is mainly inspired by the peripheral and central visual of human eyes. After that the camera motion is calculated using a robust motion estimation method. Our system is tested in indoor low-textured and large-scale outdoor environment. Experimental results show that compared to fixed stereo cameras, our bionic eyes SLAM system gains more robustness and avoid tracking failure problem when facing the low-textured environment.
Autonomous navigation for small body landing using optical and inter-spacecraft measurements

In this paper, an optical camera/inter-spacecraft measurement integrated navigation method to autonomously guide a formation of spacecraft for small body landing is proposed and the visual landmark recognition method is presented to improve the navigation performance. By introducing the relative range and line of sight (LOS) angle between spacecraft measured by radiometric and optical sensors, the states of two spacecraft are estimated, and the estimation performance degradation of velocity is reduced, which is caused by using LOS to the feature point on small body surface as the unique navigation information. The camera is used for state estimation of the spacecraft, and the inter-spacecraft measurement is introduced to improve the observability and accuracy of the onboard autonomous navigation system, which provides an effective way to guarantee the sufficient accuracy. The observability of the integrated navigation system has been proven based on the analysis of the observability matrix, which illustrates the improvement of observability of the navigation system. Furthermore, a high-accuracy center extraction method is used for crater landmark extraction to obtain accurate LOS measurement information, and the extraction error is less than 1 pixel. To verify the performance of the proposed navigation method, numerical simulations are set up. Simulation results illustrate that the novel navigation method proposed in this paper improves the observability and estimation accuracy of the navigation system.
Discussion on “Novel Supervisory Control Method for Islanded Droop-Based AC/DC Microgrids”

The authors of [1] are to be commended for the significant contribution in the area of control of islanded microgrids. The paper presents a novel supervisory control method for an islanded hybrid AC/DC microgrid (HMG) with a high renewable energy source (RES) penetration. The proposed control method adjusts the set-points of the distributed generators (DGs), battery energy storage systems (BESSs), RESs, and interlinking converter (IC) optimally to restore the frequency and voltage to nominal values while reducing the generation cost at short time intervals. The authors’ comments on the following points are appreciated: 1) Practically, microgrids experience different kinds of disturbances such as sudden loss of DGs, short circuits, equipment failures, lightning, and sudden load changes [2]–[4]. Is the proposed control approach robust against all the above kinds of disturbances? In other words, how scalable is the proposed supervisory control method? 2) In [1, Section II-A], the parameter Kp in the output active power expression (1) represents the droop slop of the active powerfrequency (P-w) of the DGs in the ac grid, but the same parameter Kp exists in the output power expression (3) of the DGs in the dc grid, what does it stand for? 3) In [1, Section II], the authors in this section have presented the static model of the islanded HMG system under consideration. Why the authors did not consider the dynamic model? Would that affect the performance of the proposed control method? 4) In [1, Section III-B], the authors have considered 12 constraints in their simulation that are actually related to the operation of the
Salient Object Detection With Importance Degree

In this article, we introduce salient object detection with importance degree (SOD-ID), which is a generalized technique for salient object detection (SOD), and propose an SOD-ID method. We define SOD-ID as a technique that detects salient objects and estimates their importance degree values. Hence, it is more effective for some image applications than SOD, which is shown via examples. The definition, evaluation procedure, and data collection for SOD-ID are introduced and discussed, and we propose its evaluation metric and data preparation, whose validity is discussed with the simulation results. Moreover, we propose an SOD-ID method, which consists of three technical blocks: instance segmentation, saliency detection, and importance degree estimation. The saliency detection block is proposed based on a convolutional neural network using the results of the instance segmentation block. The importance degree estimation block is achieved using the results of the other blocks. The proposed method accurately suppresses inaccurate saliencies and estimates the importance degree for multi-object images. In the simulations, the proposed method outperformed state-of-the-art methods with respect to the F-measure for SOD; and Spearman’s and Kendall rank correlation coefficients, and the proposed metric for SOD-ID.
Partial Discharge Localization Using Electromagnetic Time Reversal: A Performance Analysis

In this study, first, a comparison on the application of electromagnetic time reversal (EMTR) and time difference of arrival (TDoA) in partial discharge localization in power transformers is presented. A two-dimensional finite-difference time-domain simulation is used to calculate the signal recorded by the sensors. Results show that, in a transformer tank excluding its windings, both methods yield similar results in terms of location accuracy, although the EMTR method only needs one sensor to localize the partial discharge (PD) source while the TDoA method needs at least three sensors in the 2D localization problem. However, the presence of transformer windings leads to a degradation of the performance of the TDoA method if the line of sight from the source to the sensor is blocked by any of the winding blocks. On the other hand, the presence of the transformer windings has an effect on the localization of PD sources that occur between two adjacent phase windings when the distance between the outer winding distances is shorter than the minimum wavelength, <inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {min}}$ </tex-math></inline-formula>. The degradation is directly caused by the diffraction limit. It is shown that, if the distance between two adjacent phase windings is greater than <inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {min}}$ </tex-math></inline-formula>, the EMTR process can locate PD sources occurring between two adjacent phase windings with acceptable accuracy. A case of occurrence of PDs in close proximity (less than <inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {min}}$ </tex-math></inline-formula>/2) to a single metallic object is analyzed both numerically and experimentally. The analysis reveals that although a degradation in the accuracy of the localization is observed compared to the case of longer distances between the PD source and the metallic object, a reasonable localization error of 10 mm (corresponding to <inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {min}}$ </tex-math></inline-formula>/10) is obtained.
Optical Remote Sensing Image Waters Extraction Technology Based on Deep Learning Context-Unet

The optical remote sensing images are used to sea-land segmentation which is a difficult task. Due to general deep learning based methods required a large number of refined annotation, however, a large scale optical remote sensing images annotation is very expensive. Therefore, the Context-Unet is proposed to produce accurate sea-land segmentation results by a few annotated training samples. In this paper, we apply the Context-Unet network to the watershed extraction. On the basis of this, we re-compile the loss function to improve the accuracy of watershed extraction. Example: Finally, the date collected from Google Earth service is used to train and test this paper proposed Context-Unet and state-o-the-art methods. The experiments proved that the proposed method outperforms than other methods, and it can achieve 98% precision and 97% recall ratios.
Rho proteins and the expression module of their encoding genes in Magnaporthe grisea

The Rho family proteins are major members of the Ras superfamily small GTPases,conserved in the eukaryotic cells.The Rho GTPase family consists of subfamily members of Rho,Rac and Cdc42,which act as molecular switches in several signal transduction pathways,such as in the regulation of cell polarity,cell movement,cell shape,and cell-cell and cell-matrix interaction.To understand the function and evolution of the Rho family proteins in Magnaporthe grisea and other fungi,we searched the M.grisea genome database,and retrieved more than 30 small GTPase homologous proteins,and 7 putative Rho family proteins were confirmed by analyzing the presence of a set of conserved G box GDP/GTP-binding motif elements(G1-G5). We further analyzed the enconding gene expression of these proteins during the growth and development stages in M.grisea by semi-quantitive RT-PCR,and results showed that these genes have different expression(patterns). We also constructed a phylogenetic tree of Cdc42,Rac1 and Rho1 of fungi based on the sequences(available) in GenBank,the tree well reflected the evolutionary relationship of fungi.We concluded that the structure and the function of Rho family proteins have co-evoluted through the long evolutionary process of fungi.
A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC’s reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.
Implications of Short-Circuit Degradation on the Aging Process in Accelerated Cycling Tests of SiC MOSFETs

The purpose of this paper is to investigate the impact of repetitive short circuit events on the remaining useful lifetime of 1.0-kV/22-A SiC MOSFETs. Mixed accelerated power cycling tests, together with the different number of short-circuit repetitions, have been performed to provide a concrete estimation of short-circuit impact. The experimental results of short circuit waveforms show an increasing gate leakage current with the increasing number of repetitions. Due to the higher on-state voltage, induced by short circuit degradation, the devices withstand higher temperature swing during power cycling test compared to their initial condition, which accelerates the aging process and is related to the number of repetitive short circuits.
Calibration-Free Error-Related Potential Decoding With Adaptive Subject-Independent Models: A Comparative Study

Error-related potentials (ErrPs) provide an elegant method to improve human-machine interaction by detecting incorrect system behavior from the electroencephalogram of a human operator in real time. In this paper, we focus on adaptive subject-independent decoding models particularly suitable for ErrP classification. As individualized decoding models require a time-consuming calibration phase, such models provide a promising alternative. Based on an investigation of the characteristics of inter-subject variations in the signal and feature space, we evaluate the performance of a decoding model solely trained on prior data and the effectiveness of adapting this model to a new subject in a comparative study. Our results show that such a generalized model can decode ErrPs with an acceptable average accuracy of 72.7 ± 9.66% and that supervised adaptation can significantly improve the accuracy of the generalized model after adaptation with 85 trials by on average +3.8 ± 5.1%. We show that adaptation of subject-independent decoding models is superior to the traditional calibration procedure. Unsupervised adaptation only proved effective for some subjects and requires further attention to be practical for a broader range of subjects. Consequently, our work contributes to the development of calibration-free ErrP decoding in the broader scope of enhancing usability of ErrPs for human-machine interaction.
Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Human hands engage in interactive activities in many practical working scenarios, among which the interactions between human hands and objects are the most common. Tracking the movement of the human hand during hand-object interactions is an important research task that is also challenging due to the high-dimensionality and occlusions. In this paper, we track hand-object interactions from depth observations with a model-based method. To overcome the difficulties of optimum searching in the hand-object high-dimensional space, we propose a new algorithm — collaborative differential evolution filtering (CoDEF) — for tracking hand-object interactions. The proposed CoDEF algorithm integrates the differential evolution (DE) algorithm into a particle filtering (PF) framework to accelerate the convergence of particles. Particles are driven to the regions with a high probability by optimizing the matching error under the current observation with DE. To decompose the state space and decrease the complexity of optimum searching, CoDEF tracks the movement of the hand and object by using two collaborative trackers. Based on the proposed CoDEF algorithm, we develop a model-based tracking system with 3D graphic techniques. According to the experimental results, the proposed CoDEF algorithm can achieve robust tracking of hand-object interactions using fewer particles.
Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network

Due to the advantage of automatically extracting features from raw data, deep learning (DL) has been increasingly favored in the field of machine fault diagnosis. However, DL exposes the problems of large sample size and long training time, and in actual working conditions, the amount of labeled fault data available is relatively small, so a DL model of good generalization and high accuracy is difficult to be trained. In order to solve these problems, a deep transfer convolutional neural network (DTCNN) is proposed in this research. ResNet-50 is selected as the pre-trained model of deep convolutional neural network, and is transferred to solve the problem of bearing fault classification based on the idea of transfer learning. Firstly, raw fault signals are converted into time-frequency images by using continuous wavelet transform (CWT). Then, the images are further converted into RGB formats, which are used as the input of DTCNN. Finally, an end-to-end fault diagnosis model based on DTCNN is designed. The proposed method is validated on two datasets collected from motor bearing and self-priming centrifugal pump, respectively. Most sub-datasets from motor bearing show the prediction accuracies near 100%, and in the self-priming centrifugal pump dataset, we achieve improvement in accuracy from 99.48%±0.1966 to 99.98%±0.0332. The experimental results demonstrate that the proposed method outperforms other DL methods and traditional machine-learning methods.
Towards Automatic Personality Prediction Using Facebook Likes Metadata

We demonstrate that easy accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits such as the Big Five personality traits. The analysis presented based on a dataset of over 738,000 users conferred their Facebook Likes (95 million unique Like objects), social network activities, posts, egocentric network, demographic characteristics, and results of various self-reported psychometric tests. The proposed model uses a new and unique mapping technique between each Facebook Like object to their corresponding Facebook page category/sub-category object extracted from the API calls as Likes metadata, which is then evaluated as features for a set of machine learning algorithms to predict individual psychodemographic profiles from users Likes. Traditionally, entities where able to access an individual’s personality through having them fill out psychological questionnaires. In this paper, we present a method which indicates that a person’s Big Five personality score can be easily predicted by leveraging the information about the pages a person liked on Facebook.
Bifurcation Analysis and Probabilistic Energy Landscapes of Two-Component Genetic Network

Genetic oscillators play an important role in cell physiology. In this paper, we investigate the global kinetics of an activator-repressor genetic oscillator through the bifurcation analysis and probabilistic energy landscape. The results reveal that the bifurcation can induce the rich dynamics with the variation of the parameters, including monostable state, oscillations, and bistable state. These dynamics are further investigated by the probabilistic energy landscapes in which the force from the negative gradient of the energy landscapes attract the system down to the oscillation path, and the flux is the driving force of the oscillations. Besides, the probabilistic energy landscapes show that the dynamics exhibit distinct transition features when the system is close to the critical points of the oscillation region. Based on the theory of energy landscape, we also explore how the system parameters and noise affect the stability of the activator-repressor oscillations through the physical methods of barrier heights and energy dissipation. The simulation results show that the system parameters and noise intensity have a negative relation with the stability of the oscillations, and the control parameter can play a positive role in maintaining the stability of oscillations. Our methods are quite general and can be utilized to explore the potential global dynamics of more complicated gene regulatory systems.
A New Polarimetric Calibration Method for C-band Spaceborne SAR Based on Scattering Model of Amazon Rainforest

In this paper we propose a new polarimetric calibration method for C-band spaceborne SAR based on scattering model of amazon rainforest. The well-known Freeman-Durden model is used to express the polarimetric scattering mechanisms of amazon rainforest for C-band spaceborne SAR, and then nonlinear equations with polarimetric distortion parameters are built by use of covariance matrix and solved iteratively to get the polarinetric distortion matrix (PDM). In comparison with the Quegan method, no approximation is used in our method and therefore we can solve all the polarimetric distortion parameters more accurately, even if the crosstalk is high. Moreover, both channel imbalances and crosstalks can be directly estimated by our method, which is more practical as no artificial reflectors are needed to determine the channel imbalances. Simulations based on the RADARSAT-2 data confirm the validity and feasibility of the proposed method.
PRIMARY HISTIOCYTIC SARCOMA OF THE EPIDIDYMIS IN B6C3F1 MOUSE

A case of histiocytic sarcoma developed primarily in the epididymis of B6C3F1 mouse at 109 weeks old was reported. Metastases were observed in the liver and adipose tissue of the abdominal cavity. Tumor cells showed atypism, pleomorphism and active erythrophagocytosis. Touton type giant cells, which were PAS positive after diastase digestion, were seen in the tumor. Electronmicroscopically, the tumor cells had many slender processes forming interdigitation with the adjacent cells. However, no junctional complex or basal lamina was observed in the tumor cells. Tubular autolysozomes in the cytoplasm were characteristic feature of tumor cells of the present case. The vacuoles containing finger print membranes similar to the structures observed in human eosinophilic granuloma of the bone were found in some of the tumor cells. The structures similar to Birbeck's granule of the T-zone histiocyte were also observed. This case seems to be the first report on histiocytic sarcoma developed in the epididymis of B6C3F1 mice. Since fibrohistiocytic proliferative lesion developed in the epididymis of B6C3F1 mice. Since fibrohistiocytic proliferative lesions were known to develop occasionally in the epididymis of B6C3F1 mice, differential diagnosis from this proliferative lesions is important. Further immunohistological analysis for elucidating the origin and pathogenesis of this neoplastic lesion will be needed. (J Toxicol Pathol 7: 95`102, 1994)
Cross Convolutional Neural Networks

Using convolutional neural networks (CNNs) to classify images is one of the research hotspots in recent years. In order to achieve a good classification effect, the learning parameters of the convolutional neural network should be reduced as much as possible. Therefore, a new operation, cross convolutional, is introduced in this paper, which uses the principle of target calibration in YOLO algorithm and the transformation function. It generalizes convolution, reduces the volume of the model, but without affecting the ability to capture high-dimensional features. The experiments show that as the network depth increases, the cross convolutional neural network can not only reduce more learning parameters, but also achieve better classification results than baseline CNN.