Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching and learning of compliance using neural nets: representation and generation of nonlinear compliance

A new approach to the representation and learning of compliance control laws using neural networks is presented. Compliance is treated as a nonlinear mapping from a measured force to a corrected motion. The nonlinear mapping is represented by a multilayered neural network, which makes it possible to deal with complex strategies of force feedback. This network representation provides not only linear compliance, as represented by stiffness and damping matrices, but also nonlinear compliance, which has never been explored extensively. This network approach also makes it possible to teach a desired compliance from teaching data by using an iterative learning algorithm. Conventional methods of compliant motion control are reviewed, linear compliance and its limits are discussed, and nonlinear compliance based on the network representation is introduced. An analysis is made of the nonlinearity of compliance that arises in performing assembly tasks, and the network structure that meets the requirements for representing a group of nonlinear compliances necessary for performing the assembly tasks is obtained. <<ETX>>
Does Uncertainty Reduction Facilitate the Perceptual Disconfirmation of Negative Expectancies in Computer-Mediated Communication?: A Test of Two Models

This study tests two models of perceptual disconfirmation. Perceptual disconfirmation occurs when individuals have a negative expectation regarding their future conversation partners’ demeanor, but after conversing with and influencing their partners’ behavior, individuals’ assessment of their partners’ demeanor becomes more positive. This effect is, so far, novel to computer-mediated communication: When individuals with similar expectancies get acquainted using phone or face-to-face channels their negative preinteraction expectancies persist and assessments remain unchanged. This experiment explored the extent to which questions and disclosures, and partners’ disclosive responses, mediated perceptual disconfirmation. Individuals were led to believe that a dyadic conversation partner was either in a bad mood (negative and malleable expectation) or had a bad personality (negative and nonmalleable expectation). After a 30-min online conversation to get acquainted, perceptual disconfirmation emerged, although the verbal strategies did not mediate the effect. This study raises new questions about the process by which individuals who have negative expectancies of their future conversation partners defy these negative expectations and shift their attitudes to become more positive online.
Draft Genome Sequences and Complete Sequences of Two Plasmids of Escherichia coli Strain BOq 01, a Multiple-Antibiotic-Resistant Strain Isolated from a Poultry Farm in Mexico

We report here the draft genome sequence of Escherichia coli strain BOq 01, a bacterium isolated from a poultry farm; the genome includes two plasmids conferring antibiotic resistances. This bacterium has a GC content of 50.89% and a genome size of 4.6 Mb. ABSTRACT We report here the draft genome sequence of Escherichia coli strain BOq 01, a bacterium isolated from a poultry farm; the genome includes two plasmids conferring antibiotic resistances. This bacterium has a GC content of 50.89% and a genome size of 4.6 Mb.
Design of a High-Conversion-Efficiency X-Band Rectifier for Microwave Wireless Power Transmission

Microwave wireless power transmission technology has been applied to several applications with advantages, such as solar power satellites, remotely powered vehicles, surveillance platforms, wireless sensors, and radio frequency identification (RFID) tags. The design of a rectifier with high MW-DC conversion efficiency is crucial in microwave wireless power transmission technology due to its enormous influence in the overall system efficiency. Most of the studies of rectifier choose schottky diodes as the rectifying device [1]–[8]. However, due to the nonlinearity of the diode, it is hard to design a rectifier with high efficiency without large quantities of computer simulations, especially under large signals condition. Therefore, a theoretical analysis of diodes in a specific rectifier circuit is important. References [1] and [8] present two theoretical analysis methods based on one-diode circuit model. In this work, we will expand the one-diode model analysis further to voltage doubler model analysis. Mechanism of diodes in a voltage-doubler rectifier has been studied. From the diode junction voltage waveforms and diode current-voltage characteristic curves shown in Fig. 1, closed-form equations for the diode input impedance and efficiency have been derived and expressed as (1) and (2). The closed-form equations have been validated by comparing the calculated results with the simulated ones by the Advanced Design System (ADS) software. The comparison charts are shown in Fig. 2 and Fig. 3. The closed-form equations are applicable for microwave voltage-doubler rectifier and can be a useful tool for design. In this paper, a rectifier which is work at X-band has been designed and tested. The selection of diodes was based on their MW-DC conversion efficiencies which can be calculated by equation (2). The design of impedance matching network was based on its impedance which can be calculated by equation (1). The measured efficiency of the proposed rectifier is 72% which is in good agreement with the calculated results. This efficiency of the proposed rectifier ranks high on the list of the related works at X-band which are shown in Table. 1.
An Evolutionary Self-Cooperative Trust Scheme Against Routing Disruptions in MANETs

How to achieve reliable routing has always been a major issue in the design of communication networks, among which mobile ad hoc networks (MANETs) possess the most adversarial networking environment due to the absence of fixed infrastructure, the nature of open transmission media and the dynamic network topology. These characteristics also make the design of routing protocols in MANETs become even more challenging. In this paper, we propose an evolutionary self-cooperative trust (ESCT) scheme that imitates human cognitive process and relies on trust-level information to prevent various routing disruption attacks. In this scheme, mobile nodes will exchange trust information and analyze received trust information based on their own cognitive judgment. Eventually, each node dynamically evolves its cognition to exclude malicious entities. The most attractive feature of ESCT is that they cannot compromise the system even if the internal attackers know how the security mechanism works. In this paper, we evaluate the performance of ESCT scheme under various routing disruption attack situations. Simulation results affirm that ESCT scheme promotes network scalability and ensures the routing effectiveness in the presence of routing disruption attackers in MANETs.
Design and control of minimum-phase flexible arms with torque transmission mechanisms

A flexible arm with a special transmission mechanism is analyzed, and a method for integrated design of the arm structure and controller is presented. Control of flexible arms is a difficult problem because of the complicated dynamic coupled with the nonminimum-phase nature which is due to the noncollocated construction of sensor and actuator. The transmission mechanism developed relocates the torque-actuation point on the arm and modifies the flexible arm to a minimum-phase system. The resulting flexible arm is analyzed in terms of poles and zeros that are changed by relocating the torque-actuation point. A simple prototype arm is designed and compared with a nonminimum-phase arm. The effectiveness of the special transmission is discussed with regard to control performance, and an integrated structure/control design methodology is proposed. <<ETX>>
High-Resolution PolSAR Image Interpretation Based on Human Image Cognition Mechanism

Since the all-day and all-weather working characteristics, polarimetric synthetic aperture radar (PolSAR) has been widely used in many application for earth observation. PolSAR system can acquire targets scattering information under different polarization patterns and obtain detailed information, such as physical characteristics, spatial distribution and direction of the targets, etc. However, in high-resolution PolSAR images, the scattering characteristics of targets are very sophisticated, and different parts of the same target may present different scattering mechanisms. As a result, traditional interpretation technologies are sometimes not applicable in a high-resolution PolSAR scenario. Recently, many artificial intelligence algorithms that simulate the scheme of human cognition have been developed and they have great significance for efficient, intelligent, and accurate images interpretation. In this paper, based on human cognition theory and taking into account the characteristics of targets in PolSAR images, an algorithm for the interpretation of various targets in high-resolution PolSAR images is presented. In this paper, a “perception–reasoning–decision” cognition model is built following the cognition process of professional PolSAR image interpreters. In addition, with the help of theories such as multilevel image segmentation, fuzzy logic, neural network, and the context semantic characteristics, the mathematical model is established and implemented. The proposed method is performed on several sets of PolSAR images obtained under different imaging conditions and is validated by comparing with existing classification methods. The results show that the proposed method can effectively improve the identification of targets from PolSAR images.
Control Schemes for Reducing the Second Harmonic Current in Two-Stage Single-Phase Converter: An Overview from DC-Bus Port-Impedance Characterization

The instantaneous input and output power of the single-phase converter are imbalanced, resulting in second harmonic current (SHC). This paper provides an insight for reducing the SHC in two-stage single-phase converter from the perspective of the dc-bus port-impedance characterization. It is found that the dc-dc converters in the two-stage single-phase converter can be categorized into two types. One is operating as a bus-voltage-controlled converter (BVCC) whose dc-bus port-impedance is approximately inverse-proportional to the voltage loop gain. The other is operating as a bus-current-controlled converter (BCCC) whose dc-bus port-impedance is a negative resistor. Based on the dc-bus port-impedance characterization, critical points for SHC reduction are summarized, indicating that, for reducing SHC in BVCC, proper control scheme needs be incorporated for increasing the dc-bus port-impedance at twice the input frequency $(2f_{\text{in}})$ or output frequency $(2f_{\mathrm{o}});$ concurrently, the dc bus voltage ripple should be limited for reducing the SHC in the dc source or load; by contrast, for reducing SHC in BCCC, the dc bus voltage ripple should be reduced; simultaneously, the loop gain at $2fi_{\text{in}}$ or $2f_{\mathrm{o}}$ should be high enough for eliminating the SHC in the dc source or load. Thereafter, proper SHC reduction approaches are recommended for different types of two-stage single-phase converters, and pros and cons of different SHC reduction schemes are carefully reviewed. Finally, potential challenges and issues related to this research topic are discussed.
Analysis and Reduction Method of MMC Capacitor Voltage Ripple Operation Boundary based on Circulating-Current Control

Sub-module capacitors of modular multilevel converter (MMC) have critical impact on the cost and size of MMC. Circulating current control is an effective method to suppress capacitor voltage ripple. The existing analytical method is not sufficient enough to figure out accurate analytical relation between MMC operation boundaries and circulating current control. Therefore, this paper proposes an operation boundary analysis method which considers the impact of circulating-current control, and accordingly proposed a capacitor voltage ripple reduction control strategy, which can directly regulate the capacitor voltage ripple boundary through circulating-current control. Experimental results are presented to verify the validation of the proposed analysis method and control strategy.
An Evolutionary Tabu Search Algorithm for Matching Biomedical Ontologies

Since these biomedical ontologies are mostly developed independently and many of them cover overlapping domains, establishing meaningful links between them, so-called biomedical ontology matching, is critical to ensure inter-operability and has the potential to unlock biomedical knowledge by bridging related data. Due to the complexity of the biomedical ontology matching problem (large-scale optimal problem with lots of local optimal solutions), Evolutionary Algorithm (EA) can present a good methodology for determining biomedical ontology alignments. However, the slow convergence and premature convergence are two main shortcomings of EA-based ontology matching techniques, which make them incapable of effectively searching the optimal solution for biomedical ontology matching problems. To overcome this drawback, in this paper, an Evolutionary Tabu Search Algorithm (ETSA) is proposed, which introduces the Tabu Search algorithm (TS) as a local search strategy into EA's evolving process. Moreover, to efficiently solve the biomedical ontology matching problem, an biomedical concept similarity measure is presented to calculate the similarity value of two biomedical concepts and an optimal model for biomedical ontology matching is constructed. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with state-of-the-art ontology matchers show the effectiveness of ETSA.
Enabling Efficient Stroke Prediction by Exploring Sleep Related Features

Stroke is one typical chronic disease, which is caused by the degenerative disorder of the central nervous system and has a serious impact on the daily lives of human beings. Thereby, it is of great value to enable early diagnosis or prediction of stroke by monitoring peoples daily physiological data and designing useful stroke predictors when the symptoms are not apparent. Specifically, in this paper, we propose a novel approach for stroke prediction by exploring sleep related features. In the first step, we present a stroke prediction framework, which integrates common medical features with fine-grained sleep features for stroke risk prediction. In the second step, we propose a stroke risk prediction model, which consists of two key components to control the false negative rate of stroke prediction. We evaluate the framework using a real polysomnogram dataset that contains 66 patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall and AUC are 83.1%, 83.6%, and 0.782, respectively.
Learning Place Ambience from Images Using Deep ConvNet

Many studies have found that the ambience of a place has a significant effect on the satisfaction or behavioral intention of its visitors. To utilize the atmospheric characteristics of places, in this paper, we present a novel method to recognize the ambience of a place from images that are taken at a place based on a deep convolutional neural network (ConvNet). We trained our model such that it can infer place ambience without any help from other feature extractors. By transferring generic visual features, we improve the performance as well. Experiments were done on the public dataset shared on the Yelp Dataset Challenge. The results show that the proposed method can recognize the place ambience better than existing methods.
Coexistence of Full-Duplex-Based IEEE 802.15.4 and IEEE 802.11

As various wireless devices share the same frequencies in the unlicensed 2.4-GHz industrial scientific medical band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the guide busy tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multinode case as well.
On the Modeling and Diagnosis of a Micro-Structured Wire Antenna System

Micro-structured systems of wire antennas the inter-element distance of which being much smaller than the electromagnetic wavelengths either in transmission or reception are studied by the authors from 3-D computational modeling to experiments in anechoic chambers on down-scaled prototypes. Recent results with focus on potential super-localization of a peculiar radiating element in the aforementioned micro-structure will be considered here while further challenges ahead, notably in terms of questions of super-resolved imaging in likewise microstructured structures will be sketched.
Communicative functions of emoji sequences on Sina Weibo

The popular press is currently rife with speculation that emoji are becoming a global, digitally-mediated language. Sequences of emoji that function like verbal utterances potentially lend strong support to this claim. We employ computer-mediated discourse analysis to analyze the pragmatic meanings conveyed through emoji sequences and their rhetorical relations with accompanying text, focusing on posts by social media influencers and their followers on a popular Chinese social media platform. The findings show that the emoji sequences can function pragmatically like verbal utterances and form relations with textual propositions, although their usage differs from textual utterances in several respects. We also observed user innovations that make the sequences more language like, although there is not as yet a fixed grammar of emoji sequences. We characterize this emoji use as an emergent graphical language, with the caveats that it is not yet a fully-formed language and that the Chinese emoji language that is emerging is different from the English variety, and therefore emoji are not a universal language. In order to promote the further development of emoji language(s), we advance recommendations for emoji design grounded in linguistic principles.
Seafloor Mapping by 360 Degree View Camera with Sonar Supports

Seafloor mapping is usually done by many kinds of sonars. Recently, the mapping is tried by cameras and laser scanners whose resolution is higher in short range than sonars. But the area illuminated by floodlights isn't large underwater and there is few or no ambient light over 100 m depth. Therefore, in deep water, camera view is much smaller than the original Field of View. To maximize the performance of underwater observation with cameras, we developed an image capturing processing and visualization system in collaboration with Nissan Motor Company that builds upon and extends the technology used in their Around View Driving Assistance System. The view of the system covers all of the lower hemisphere, and records the view and undulations of seafloor using sonar.
Synthetic-Inertia-Based Modular Multilevel Converter Frequency Control for Improved Micro-Grid Frequency Regulation

High penetration of renewable energies through fast-response power converters results in a considerable displacement of conventional synchronous generators and losing of system inertia for frequency control. In a micro-grid with high shares of renewables, Modular Multilevel Converters (MMCs) can be employed serving as an interface between the renewable generation and the micro-grid. The MMC submodule (SM) capacitors can be used as energy storages to provide an considerable amount of synthetic inertia for system frequency support. This paper proposes an MMC synthetic inertia concept that is mainly affected by renewable penetration ratio, SM capacitance and the modulation index of the MMC, with the system constraints determined in the design phase. A substantial portion of the system inertia can be provided by the MMC with properly selected system parameters. By simultaneously controlling the MMC capacitor voltage and output active power based on the system frequency variation, an amount of energy can be released or absorbed by SM capacitors to improve the system frequency response during the transients of frequency event, while the renewable power is scarcely influenced. The proposed concept is verified by simulations and experiments. The experimental results show that, by proper system parameters design, the frequency deviation and the rate of change of frequency (RoCoF) can be reduced by 13.9% and 18.5%, respectively.
Remote Sensing Image Registration Based on Fuzzy Shape Context Feature and Local Space Vector Similarity Constraint

Remote sensing image registration is a fundamental and challenging problem, and it is a critical prerequisite in a wide range of applications including environment monitoring, change detection, image fusion, image mosaic, and map updating. Remote sensing image registration is a key technology for dynamic monitoring of a city. In order to promote remote sensing image registration precision, we propose a remote sensing image registration method based on fuzzy shape context feature and local space vector similarity constraint. There are two main contributions: (1) Fuzzy shape context feature (2) Local spatial vector similarity constraints based on local vector feature. Meanwhile, we evaluated the performances of the proposed method and compared with four state-of-the-art methods (SIFTCPDR-SOCGLMDTPS) where our method shows the best alignments in most cases.
Online Condition Monitoring of Bond Wire Degradation in Inverter Operation

This paper presents a condition monitoring system for the online detection of the degradation of power switches in medium power inverter topologies. The monitoring, suitable for MOSFET and IGBTs, is based on the measurement of both on-state voltage of the semiconductor and the voltage drop in the bond wires. Through these measurements, the main failure mechanisms, been these the degradation of the solder joints and the bond wire connections, can be anticipated. The proposed system has been implemented in a setup and tests have been performed in static conditions and as an inverter, with a switching frequency up to 5 kHz. Moreover, this paper proposes an alternative routine appropriate for those applications in which a higher switching frequency is required. This routine has been validated with the DUT switching at 50 kHz. In all the circumstances, the prototype proved its efficiency to measure accurately and its capability to detect the bond wire degradation.