Advances in graphics and machine learning algorithms and processes have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens democracies around the world by enabling wide-spread distribution of false information to billions of individuals via social media platforms. One approach to thwarting the flow of fake media is to detect synthesized or modified media via the use of pattern recognition methods, including statistical classifiers developed via machine learning. While detection may help in the short-term, we believe that it is destined to fail as the quality of the fake media generation continues to improve. Within a short period of time, neither humans nor algorithms will be able to reliably distinguish fake versus real content. Thus, pipelines for assuring the source and integrity of media will be required---and will be increasingly relied upon. We propose AMP, a system that ensures authentication of a media content's source via provenance. AMP creates one or more manifests for a media instance uploaded by a content provider. These manifests are stored in a database allowing fast lookup using the AMP service from applications such as browsers. For reference, the manifests are also registered and signed by a permissioned ledger implemented using the Confidential Consortium Framework (CCF). CCF employs both software and hardware techniques to ensure the integrity and transparency of all registered manifests. AMP, through its use of CCF, allows for a consortium of media providers to govern the service while making all governance operations auditable. The authenticity of the media can be communicated to the user via an icon or other visual elements in the browser, indicating that an AMP manifest has been successfully located and verified.
Port Knocking is a method for authenticating clients through a closed stance firewall, and authorising their requested actions, enabling severs to offer services to authenticated clients, without opening ports on the firewall. Advances in port knocking have resulted in an increase in complexity in design, preventing port knocking solutions from realising their potential. This paper proposes a novel port knocking solution, named Crucible, which is a secure method of authentication, with high usability and features of stealth, allowing servers and services to remain hidden and protected. Crucible is a stateless solution, only requiring the client memorise a command, the server's IP and a chosen password. The solution is forwarded as a method for protecting servers against attacks ranging from port scans, to zero-day exploitation. To act as a random oracle for both client and server, cryptographic hashes were generated through chaotic systems.
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.
Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card without your awareness. An important assumption of today's Near-field Communication (NFC) enabled cards is the limited communication range between the commercial reader and the NFC cards -- a distance below 5~cm. Previous approaches to attacking this assumption effectively use mobile phones and active relays to enlarge the communication range, in order to attack the NFC cards. However, these approaches require a power supply at the adversary side, and can be easily localized when mobile phones or active relays transmit NFC signals.
We propose ReCoil, a system that uses wearable passive relays to attack NFC cards by expanding the communication range to 49.6 centimeters, a ten-fold improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-Ant Colony Algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of human body, we then design a half waist band by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays composed of common copper wires and tunable capacitors expand the range of NFC attacks to 49.6 centimeters.
The study of Proof of Work (PoW) has culminated with the introduction of cryptocurrency blockchains like Bitcoin. These protocols require their operators, called miners, to expend computational resources and they reward them with minted cryptocurrency tokens. The system is secure from attackers who cannot expend resources at a rate equivalent to that of all benign miners. But the resource requirement is arbitrary - the product of the number of minted tokens and their real value. We present Hybrid Expenditure Blockchain (HEB), a novel cryptocurrency PoW protocol that allows its designer to tune external expenditure. To the best of our knowledge, this is the first tunable PoW protocol. Despite the reduced resource expenditure, it maintains the security guarantees of pure PoWprotocols against rational attacks. HEB has practical implications, as global power expenditure on PoW blockchains exceeds that of a medium-sized country. Applying HEB in operational PoW systems can significantly reduce their ecological footprint.
Sibling rivalry is commonplace within animal families, yet offspring can also work together to promote each other’s fitness. Here we show that the extent of parental care can determine whether siblings evolve to compete or to cooperate. Our experiments focus on the burying beetle Nicrophorus vespilloides, which naturally provides variable...
In PNAS, Fayet et al. (1) report on two cases of tool use in a seabird. In two distant populations they recorded Arctic puffins (Fratercula arctica) using sticks to scratch themselves (Fig. 1). The documentation of tool use in this species expands the ever-growing list of tool-using birds through rare...
Nature, Published online: 22 January 2020; doi:10.1038/s41586-020-1947-zAn in vivo approach to identify proteins whose enrichment near cardiac CaV1.2 channels changes upon β-adrenergic stimulation finds the G protein Rad, which is phosphorylated by protein kinase A, thereby relieving channel inhibition by Rad and causing an increased Ca2+ current.
Cherry blossoms line the Saturday Market near Burnside. Back home, winter
This randomized clinical trial compares the effects of pancreatic drainage surgery within 6 weeks vs a stepped medical-endoscopy-surgical approach on pain score and relief over 18 months among patients with chronic pancreatitis.
To the Editor The Viewpoint on organ donation policy maintained that the US opt-in organ donation system performs among the best in the world. Donors per potential eligible donor were used as evidence despite this metric being criticized for potentially overestimating organ procurement organization (OPO) performance. Organ procurement organizations self-regulate and may be incentivized to modify eligibility of missed cases to meet performance requirements. Also, countries with opt-out programs consider cases when death occurs unexpectedly, often termed uncontrolled donation after circulatory determination of death. Implementing uncontrolled donation after circulatory determination of death increases organ supply but with more attrition. Countries that consider higher-risk candidates have fewer donors per potential eligible donor compared with the United States, which has stricter criteria.
This Viewpoint discusses barriers to collecting patient-reported outcome measures for symptom monitoring as a component of the Oncology Care First payment model, a Centers for Medicare & Medicaid Services program that provides bundled population payments to cover physician services and enhancements intended to improve care quality for patients receiving systemic cancer treatment, and proposes ways to implement and facilitate the requirement given its importance to patient well-being.
This JAMA Insights Women’s Health reviews the epidemiology, evaluation, and management of primary dysmenorrhea, with a focus on use of combined hormonal oral contraceptive pills for symptom management.
New research reveals for the first time that UV combined with visible and infrared light cause damage to the skin and we need to protect our skin against all three to prevent aging.
Hyung Joon Cha and his research team identified a mechanism of adhesive proteins in a mussel that controls the surface adhesion and cohesion.They substantiated the synergy of molecules in adhesive proteins. Their new discovery is expected to be applied in making stronger underwater bioadhesive than the conventional ones.
Policies and new technologies have reduced emissions of precursor gases that lead to ozone air pollution, but despite those improvements, the amount of ozone that plants are taking in has not followed the same trend, according to Florida State University researchers.
A group of scientists has uncovered a previously unknown way that our genes are made into reality. Rather than directions going one-way from DNA to RNA to proteins, the latest study shows that RNA itself modulates how DNA is transcribed--using a chemical process that is increasingly apparent to be vital to biology. The discovery has significant implications for our understanding of human disease and drug design.
Regenstrief Institute and Indiana University School of Medicine researchers have identified blood-based biomarkers associated with both delirium duration and severity in critically ill patients. This finding opens the door to easy, early identification of individuals at risk for longer delirium duration and higher delirium severity and could potentially lead to new treatments of this brain failure for which drugs have been shown to be largely ineffective.
Nature, Published online: 20 January 2020; doi:10.1038/d41586-020-00142-0Young people must pursue climate fight with politicians, not through the courts, says ruling. Plus, a call to create contagious climate optimism and US research struggles for clarity over embryo-like structures.
FADS1 and FADS2 Polymorphisms Modulate Fatty Acid Metabolism and Dietary Impact on Health [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 21-44, August 2019.
The Benefits and Risks of Iron Supplementation in Pregnancy and Childhood [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 121-146, August 2019.
Bile Acids as Metabolic Regulators and Nutrient Sensors [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 175-200, August 2019.
Mitochondrial DNA Mutation, Diseases, and Nutrient-Regulated Mitophagy [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 201-226, August 2019.
Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 291-315, August 2019.
Advancing network technologies allow the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove their data consumption's adjustment by using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, data usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.
Power quality (PQ) events are referred to any abnormal deviation from the standard sinusoidal behavior of power signals within a power system. PQ events are usually studied by tracking the behavior of voltage signals over observation points of the system. IEEE Standards have defined standard categories for PQ events based on their time behavior. Each class of these events may have different level of importance from different contributors’ perspective (utilities, system operators, or costumers). Due to increasing the usage of sensitive technological loads such as transportation, banking systems, and databases on one hand in addition to the uncertainty injected to the system from aggregation of renewables on the other hand, the fast and reliable PQ events classification is an important monitoring task in the future smart grid. In this paper, combining the theory of sparse recovery with a new high-dimensional convex hull approximation framework we have developed a fast, reliable, and adaptive PQ events classification methodology named “compressive-informative sparse representation-based” PQ events classifier. Unlike usual classification approaches, the proposed classifier does not need any training procedure while due to its linear mathematical formulation acts inherently fast. Moreover, it can be easily adapted to recognize the challenging combined PQ events in addition to any permanent change in the behavior of PQ patterns.
DC–DC converter systems have drawn extensive research attentions and shown upward tendencies for industrial and military applications. In this article, a novel digital fast terminal sliding mode control (FTSMC) approach is investigated for dc–dc buck converters with mismatched disturbances. Specifically, the approximated discrete-time model of the converters with multiple disturbances is first obtained and analyzed based on the Euler's discretization method. Then, by adopting the delayed estimation technique, it is easy to obtain the accurate estimations of the lumped disturbances. Integrating disturbance compensations into the modified digital fast terminal sliding mode surface, the proposed controller is finally constructed on the basis of equivalent control method and the performance analysis is presented. Both simulation and experimental comparisons are made for the proposed digital FTSMC approach and the existing discretized linear sliding mode controllers to validate the effectiveness and feasibility of the presented controller. The proposed FTSMC approach is characterized by higher voltage tracking accuracy and better dynamic properties in different operating conditions.
In the era of Internet of people and things, big data are merging. Conventional computation algorithms including correlation measures become inefficient to deal with big data problems. Motivated by this observation, we present three fuzzy correlation measurement algorithms, that is, the centroid-based measure, the integral-based measure, and the α-cut-based measure using fuzzy techniques. Data of Shanghai stock price index (SSI) and exchange rates of main foreign currencies over China Yuan from 22 January 2013 to 17 May 2018 are used to check the effectiveness of our algorithms, and, more importantly, to observe the causality relationship between SSI and these main exchange rates. We have observed some findings as follows. First, the usage of the highest, lowest, or closing values in daily exchange rates and stock prices has impact on the significant Granger causes of exchange rates over SSI, but does not produce any opposite cause from SSI to exchange rates. Second, no matter which of our fuzzy measurement algorithms is used, Hongkong Dollar over China Yuan and U.S. Dollar over China Yuan are positively related with SSI, and Euro over China Yuan negatively correlated with SSI is always recognized as a Granger cause to SSI with the significance level being 1%. Finally, both the optimism level and the uncertainty level are observed having impact on the correlation coefficients, but the later brings more significant changes to results of the Granger causality tests.
Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13$%$.
Neddylation is a ubiquitination-like pathway that controls cell survival and proliferation by covalently conjugating NEDD8 to lysines in specific substrate proteins. However, the physiological role of neddylation in mammalian metabolism remains elusive, and no mitochondrial targets have been identified. Here, we report that mouse models with liver-specific deficiency of NEDD8...
Nature, Published online: 16 January 2020; doi:10.1038/s41586-020-1953-1Structure of the neurotensin receptor 1 in complex with β-arrestin 1
Nature, Published online: 16 January 2020; doi:10.1038/d41586-020-00083-8Young wolves’ enthusiasm for retrieving toys hints at an ability to read cues from humans.
Nature, Published online: 15 January 2020; doi:10.1038/d41586-020-00039-yMicroorganisms related to lineages of the Asgard archaea group are thought to have evolved into complex eukaryotic cells. Now the first Asgard archaeal species to be grown in the laboratory reveals its metabolism and cell biology.
Doctors have been left unprepared and climate change has not been considered a health issue. Sophie Cousins reports.
On a morning in a colder than average winter in Salem, Missouri, USA, a stranger approached my husband and said, “I guess this cold snap blows that global warming theory out of the water.” Salem is a town of about 5000 people, the county seat of Dent County. Most of the county's residents are white. More than 80% of voters chose Donald Trump for US President. When my husband hopped out of his VW camper with his full white beard and a baseball cap that read Powell's Books, he looked like what he is: a philosophy professor from the University of Missouri on his way to fish the Current River.
When children in Ratodero, a small town in the Sindh province of Pakistan, suddenly became ill in the early months of 2019, HIV was not a cause anyone would have suspected. Unlike many other infectious diseases in Pakistan, HIV prevalence, especially in children, has been relatively low.1 However, in April, 2019, local journalists reported that the children were indeed infected with HIV. Further investigations revealed that many of the children were being cared for by a self-proclaimed, low-cost paediatrician (whose qualifications have not been established) who had been reusing needles and telling patients that they were too poor to afford new needles.
Marian Knight and colleagues1 showed that a single dose of prophylactic antibiotic effectively prevented infection after operative vaginal delivery. However, to apply these results to daily clinical practice, the selection of antibiotics and candidates receiving prophylaxis for infection after operative vaginal delivery should be carefully considered. According to their previous report,2 Knight and colleagues chose co-amoxiclav as a prophylactic antibiotic after operative vaginal delivery because it has adequate coverage of group A streptococcus and is less likely to select for antibiotic resistance, such as meticillin-resistant Staphylococcus aureus and extended-spectrum β-lactamases-producing Gram-negative bacteria.
Despite declining age-standardised incidence and mortality, sepsis remains a major cause of health loss worldwide and has an especially high health-related burden in sub-Saharan Africa.
The adaptation of eukaryotic cells to anaerobic conditions is reflected by substantial changes to mitochondrial metabolism and functional reduction. Hydrogenosomes belong among the most modified mitochondrial derivative and generate molecular hydrogen concomitant with ATP synthesis. The reduction of mitochondria is frequently associated with loss of peroxisomes, which compartmentalize pathways that...
Uncovering mechanisms of protein function is challenging when structural characterization of the functionally relevant states is elusive, for instance for large and flexible proteins which resist crystallization. This is the case for structural maintenance of chromosomes (SMC) proteins and kleisin subunits which are crucial for the segregation of chromosomes during...
We study the trade-off between secret message (SM) and secret key (SK) rates, simultaneously achievable over a state-dependent (SD) wiretap channel (WTC) with non-causal channel state information (CSI) at the encoder. This model subsumes other instances of CSI availability as special cases, and calls for efficient utilization of the state sequence for both reliability and security purposes. An inner bound on the semantic-security (SS) SM-SK capacity region is derived based on a superposition coding scheme inspired by a past work of the authors. The region is shown to attain capacity for a certain class of SD-WTCs. SS is established by virtue of two versions of the strong soft-covering lemma. The derived region yields an improvement upon the previously best known SM-SK trade-off result reported by Prabhakaran et al., and, to the best of our knowledge, upon all other existing lower bounds for either SM or SK for this setup, even if the semantic security requirement is relaxed to weak secrecy. It is demonstrated that our region can be strictly larger than those reported in the preceding works.
Person re-identification (Re-ID) aims at matching person images captured in non-overlapping camera views. To represent person appearance, low-level visual features are sensitive to environmental changes, while high-level semantic attributes, such as “short-hair” or “long-hair”, are relatively stable. Hence, researches have started to design semantic attributes to reduce the visual ambiguity. However, to train a prediction model for semantic attributes, it requires plenty of annotations, which are hard to obtain in practical large-scale applications. To alleviate the reliance on annotation efforts, we propose to incrementally generate Deep Hidden Attribute (DHA) based on baseline deep network for newly uncovered annotations. In particular, we propose an auto-encoder model that can be plugged into any deep network to mine latent information in an unsupervised manner. To optimize the effectiveness of DHA, we reform the auto-encoder model with additional orthogonal generation module, along with identity-preserving and sparsity constraints. 1) Orthogonally generating: In order to make DHAs different from each other, Singular Vector Decomposition (SVD) is introduced to generate DHAs orthogonally. 2) Identity-preserving constraint: The generated DHAs should be distinct for telling different persons, so we associate DHAs with person identities. 3) Sparsity constraint: To enhance the discriminability of DHAs, we also introduce the sparsity constraint to restrict the number of effective DHAs for each person. Experiments conducted on public datasets have validated the effectiveness of the proposed network. On two large-scale datasets, i.e., Market-1501 and DukeMTMC-reID, the proposed method outperforms the state-of-the-art methods.
Learning an effective distance measurement between sample pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negative samples usually account for the tiny minority in the training set, which may fail to fully describe the data distribution close to the decision boundary. In this paper, we present a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the original negative samples, which is widely applicable to existing supervised deep metric learning algorithms. Different from existing sampling strategies which simply ignore numerous easy negatives, our DAML aim to exploit them by generating synthetic hard negatives adversarial to the learned metric as complements. We simultaneously train the feature embedding and hard negative generator in an adversarial manner, so that adequate and targeted synthetic hard negatives are created to learn more precise distance metrics. As a single transformation may not be powerful enough to describe the global input space under the attack of the hard negative generator, we further propose a deep adversarial multi-metric learning (DAMML) method by learning multiple local transformations for more complete description. We simultaneously exploit the collaborative and competitive relationships among multiple metrics, where the metrics display unity against the generator for effective distance measurement as well as compete for more training data through a metric discriminator to avoid overlapping. Extensive experimental results on five benchmark datasets show that our DAML and DAMML effectively boost the performance of existing deep metric learning approaches through adversarial learning.
We propose Mask SSD, an efficient and effective approach to address the challenging instance segmentation task. Based on a single-shot detector, Mask SSD detects all instances in an image and marks the pixels that belong to each instance. It consists of a detection subnetwork that predicts object categories and bounding box locations, and an instance-level segmentation subnetwork that generates the foreground mask for each instance. In the detection subnetwork, multi-scale and feedback features from different layers are used to better represent objects of various sizes and provide high-level semantic information. Then, we adopt an assistant classification network to guide per-class score prediction, which consists of objectness prior and category likelihood. The instance-level segmentation subnetwork outputs pixel-wise segmentation for each detection while providing the multi-scale and feedback features from different layers as input. These two subnetworks are jointly optimized by a multi-task loss function, which renders Mask SSD direct prediction on detection and segmentation results. We conduct extensive experiments on PASCAL VOC, SBD, and MS COCO datasets to evaluate the performance of Mask SSD. Experimental results verify that as compared with state-of-the-art approaches, our proposed method has a comparable precision with less speed overhead.
Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets – particularly with multi-layered structures, e.g., convolutional neural networks – or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the “synthesis” signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computational tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.
Tone Mapping Operators (TMO) designed for videos can be classified into two categories. In a first approach, TMOs are temporal filtered to reduce temporal artifacts and provide a Standard Dynamic Range (SDR) content with improved temporal consistency. This however does not improve the SDR coding Rate Distortion (RD) performances. A second approach is to design the TMO with the goal of optimizing the SDR coding rate-distortion performances. This second category of methods may lead to SDR videos altering the artistic intent compared with the produced HDR content. In this paper, we combine the benefits of the two approaches by introducing new Weighted Prediction (WP) methods inside the HEVC SDR codec. As a first step, we demonstrate the interest of the WP methods compared to TMO optimized for RD performances. Then we present the newly introduced WP algorithm and WP modes. The WP algorithm consists in performing a global motion compensation between frames using an optical flow, and the new modes are based on non linear functions in contrast with the literature using only linear functions. The contribution of each novelty is studied independently and in a second time they are all put in competition to maximize the RD performances. Tests were made for HDR backward compatible compression but also for SDR compression only. In both cases, the proposed WP methods improve the RD performances while maintaining the SDR temporal coherency.
Finite set model predictive torque control (FCSMPTC) of induction machines has received widespread attention in recent years due to its fast dynamic response, intuitive concept, and ability to handle nonlinear constraints. However, FCSMPTC essentially belongs to the open-loop control paradigm, and unmatched parameters inevitably cause electromagnetic torque tracking error. In addition, the outer loop (i.e., the speed loop) based on a proportional-integral (PI) regulator cannot achieve optimal control between speed dynamic response and torque tracking error compensation. The traditional control paradigm is abbreviated as PI-MPTC. In order to solve the aforementioned problem, this paper proposes active disturbance-rejection-based model predictive torque control (ADR-MPTC). First, the influence mechanism of mismatched parameters on torque prediction error in PI-MPTC is studied, and then the performance of a traditional PI regulator used to compensate for torque prediction error is analyzed. Second, this paper introduces several parts of the proposed ADR-MPTC, including the design of the torque prediction error observer, nonlinear prediction error compensation strategies, an enhanced predictive torque control, and a simplified full-order flux observer. Finally, PI-MPTC and ADR-MPTC are studied experimentally. The experimental results show that compared with PI-MPTC, ADR-MPTC performs better in dynamic and steady states, and has stronger robustness.
For high-speed permanent magnet machines (HSPMMs), the permanent magnet (PM) is more likely to suffer irreversible demagnetization because the heat dissipation is serious in the HSPMMs, especially for the high-power machines. This paper focuses on the comprehensive research results on the power loss and thermal characteristic for a high-power HSPMM. First, the power loss at the rated load is investigated by finite-element analysis. Then, the temperature distribution of four cooling schemes is compared by the electromagnetic-thermal iteration calculation. The effect of different parameters on thermal behavior is obtained to reduce rotor temperature, which includes an examination of the axial flow duct, cooling medium, sleeve thickness, and sleeve thermal conductivity. Finally, an improved loss separation method is employed to obtain the loss distribution from the measured total loss, and the comprehensive experiments are implemented based on one HSPMM prototype (800 kW, 15 000 rpm) to verify the related theoretical analysis.
Differential power processing (DPP) is a promising architecture to solve the issue caused by mismatches among photovoltaic (PV) submodules. To eliminate the mismatch power losses, this paper presents an optimized PV-to-bus DPP system with submodule-level individual maximum power point tracking (MPPT) and module-level minimum-power tracking (MPT) simultaneous implementation according to the built mathematical model of the power processed by DPP converters with respect to the string current. Furthermore, a flexible time-sharing algorithm is adopted to reduce the number of MPPT units. Compared with other solutions, such as PV optimizer, the proposed scheme can reduce the effects of mismatches to the minimum regardless of mismatch conditions. The system cost is reduced since only one adaptive MPPT controller is required. Besides, the system efficiency is improved due to the true MPPT implementation and MPT control. Both simulation and experimental tests are provided to validate the effectiveness of the proposed scheme.
The fast detection of magnitude, frequency, and phase angle of the fundamental-frequency component is very useful in many three-phase power system applications. Most techniques to accomplish this task are based on different versions of three-phase phase-locked loop (PLL) schemes. Some variables of three-phase systems may experience phase angle jumps after some disturbances. In this paper, a method for the fast detection of phase angle jump occurrence, estimation of the phase angle jump, and its use for improving the performance of the generalized delayed signal cancelation (GDSC) PLL is proposed. The proposed method is based on the detailed evaluation of the GDSC filter transient behavior after a phase jump. Results considering typical three-phase grid disturbances are used to evaluate the proposed scheme, in comparison with other usual PLL techniques. The use of the proposed phase jump angle estimator allowed a reduction in the GDSC-PLL convergence time of about 50%.
Time delay control (TDC) is a promising technique for robot manipulators because it is model-free, efficient, and yet, robust. Nevertheless, when a robot operates under significant payload changes, it is difficult to achieve satisfactory performance with a constant gain. To cope with this problem, several adaptive rules have already been proposed thus far, but they are less effective to significant payload changes, and parameter tuning procedures are too complicated. In this paper, we propose an adaptive gain dynamics that is more effective in payload changes and yet simpler to implement. Through simulations using a one-link arm and experiments using a whole arm manipulator with payload changes, the proposed dynamics was compared with the conventional TDC and two other existing methods. Simulation results show that the proposed algorithm can adapt to significant payload changes, achieving better accuracy than the conventional TDC. Experimental results show that the proposed method has consistently better adaptation capability than other methods, achieving significantly better accuracy. In addition, the proposed method is simpler to implement, having only one tuning parameter, whereas the existing methods require four or five such tuning parameters.
Evolutionary Models of Preference Formation [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 329-354, August 2019.
Social Networks in Policy Making [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 473-494, August 2019.
The International Monetary and Financial System [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 859-893, August 2019.
Universal Basic Income: Some Theoretical Aspects [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 895-928, August 2019.
Universal Basic Income in the United States and Advanced Countries [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 929-958, August 2019.
The Role of Space in the Formation of Social Ties [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 111-132, July 2019.
The Social Structure of Time: Emerging Trends and New Directions [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 301-320, July 2019.
Retail Sector Concentration, Local Economic Structure, and Community Well-Being [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 321-343, July 2019.
Analyzing Age-Period-Cohort Data: A Review and Critique [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 467-492, July 2019.
The Economics and Politics of Preferential Trade Agreements [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 75-92, May 2019.
The Politics of Housing [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 165-185, May 2019.
Bias and Judging [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 241-259, May 2019.
Climate Change and Conflict [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 343-360, May 2019.
Cysteine-Based Redox Sensing and Its Role in Signaling by Cyclic Nucleotide–Dependent Kinases in the Cardiovascular System [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 63-87, February 2019.
Unexpected Roles for the Second Brain: Enteric Nervous System as Master Regulator of Bowel Function [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 235-259, February 2019.
Biomarkers of Acute and Chronic Kidney Disease [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 309-333, February 2019.
Regulation of Blood and Lymphatic Vessels by Immune Cells in Tumors and Metastasis [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 535-560, February 2019.
The Physiology of Optimizing Health with a Focus on Exercise as Medicine [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 607-627, February 2019.
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