This paper develops new methods to model and control the aggregated power demand from a population of thermostatically controlled loads, with the goal of delivering services such as regulation and ...load following. Previous work on direct load control focuses primarily on peak load shaving by directly interrupting power to loads. In contrast, the emphasis of this paper is on controlling loads to produce relatively short time scale responses (hourly to sub-hourly), and the control signal is applied by manipulation of temperature set points, possibly via programmable communicating thermostats or advanced metering infrastructure. To this end, the methods developed here leverage the existence of system diversity and use physically-based load models to inform the development of a new theoretical model that accurately predicts – even when the system is not in equilibrium – changes in load resulting from changes in thermostat temperature set points. Insight into the transient dynamics that result from set point changes is developed by deriving a new exact solution to a well-known hybrid state aggregated load model. The eigenvalues of the solution, which depend only on the thermal time constant of the loads under control, are shown to have a strong effect on the accuracy of the model. The paper also shows that load heterogeneity – generally something that must be assumed away in direct load control models – actually has a positive effect on model accuracy. System identification techniques are brought to bear on the problem, and it is shown that identified models perform only marginally better than the theoretical model. The paper concludes by deriving a minimum variance control law, and demonstrates its effectiveness in simulations wherein a population of loads is made to follow the output of a wind plant with very small changes in the nominal thermostat temperature set points.
This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes ...PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand "valley". The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation.
► Toxicity of AgNPs is dependent on the silver ion fraction in the AgNP suspension. ► At high silver ion fraction AgNPs do not add significant additional toxicity. ► Cell cycle and amount of ...apoptotic cells is the same at high silver ion fraction. ► At low silver ion fraction the AgNP suspension is more toxic than its supernatant. ► Measurement of silver ion fraction of AgNPs is crucial for toxicity studies.
The toxicity of silver nanoparticles (AgNPs) has been shown in many publications. Here we investigated to which degree the silver ion fraction of AgNP suspensions, contribute to the toxicity of AgNPs in A549 lung cells. Cell viability assays revealed that AgNP suspensions were more toxic when the initial silver ion fraction was higher. At 1.5
μg/ml total silver, A549 cells exposed to an AgNP suspension containing 39% silver ion fraction showed a cell viability of 92%, whereas cells exposed to an AgNP suspension containing 69% silver ion fraction had a cell viability of 54% as measured by the MTT assay. In addition, at initial silver ion fractions of 5.5% and above, AgNP-free supernatant had the same toxicity as AgNP suspensions. Flow-cytometric analyses of cell cycle and apoptosis confirmed that there is no significant difference between the treatment with AgNP suspension and AgNP supernatant. Only AgNP suspensions with silver ion fraction of 2.6% or less were significantly more toxic than their supernatant as measured by MTT assays. From our data we conclude that at high silver ion fractions (≥5.5%) the AgNPs did not add measurable additional toxicity to the AgNP suspension, whereas at low silver ion fractions (≤2.6%) AgNP suspensions are more toxic than their supernatant.
•A novel framework for multi-site fMRI analysis without data-sharing using privacy-preserving federated learning.•The first employment of domain adaptation techniques on federated learning ...formulation for medical image analysis.•Comparisons to baseline strategies and innovative model evaluation methods from the biomarker interpretation perspective.•New insights into utilizing multi-site medical data to improve both tasks performance and replicable and informative biomarker detection.•Potential solution to training deep learning models on multiple small, heterogeneous, privacy-sensitive medical datasets.
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Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities’ data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.
India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the ...electricity and carbon mitigation costs to reliably operate India's grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India's proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India's specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India's 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈ 45% share by energy) could result in electricity costs similar to a fossil fuel-dominated system. As an alternative strategy for meeting peak electricity demand, battery storage can avert the need for new fossil fuel capacity but is cost effective only at low capital costs (≈ USD 150 per kWh).
Although many users outsource their various data to clouds, data security and privacy concerns are still the biggest obstacles that hamper the widespread adoption of cloud computing. Anonymous ...attribute-based encryption (anonymous ABE) enables fine-grained access control over cloud storage and preserves receivers’ attribute privacy by hiding attribute information in ciphertexts. However, in existing anonymous ABE work, a user knows whether attributes and a hidden policy match or not only after repeating decryption attempts. And, each decryption usually requires many pairings and the computation overhead grows with the complexity of the access formula. Hence, existing schemes suffer a severe efficiency drawback and are not suitable for mobile cloud computing where users may be resource-constrained.
In this paper, we propose a novel technique called match-then-decrypt, in which a matching phase is additionally introduced before the decryption phase. This technique works by computing special components in ciphertexts, which are used to perform the test that if the attribute private key matches the hidden access policy in ciphertexts without decryption. For the sake of fast decryption, special attribute secret key components are generated which allow aggregation of pairings during decryption. We propose a basic anonymous ABE construction, and then obtain a security-enhanced extension based on strongly existentially unforgeable one-time signatures. In the proposed constructions, the computation cost of an attribute matching test is less than one decryption operation, which only needs small and constant number of pairings. Formal security analysis and performance comparisons indicate that the proposed solutions simultaneously ensure attribute privacy and improve decryption efficiency for outsourced data storage in mobile cloud computing.
Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many ...advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.
This paper explores methods to coordinate aggregations of thermostatically controlled loads (TCLs; including air conditioners and refrigerators) to manage frequency and energy imbalances in power ...systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. We compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We use Markov chain models to describe the temperature state evolution of populations of TCLs, and Kalman filtering for both state and joint parameter/state estimation. A look-ahead proportional controller broadcasts control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26%-9.3% of steady state aggregated power consumption. We also report results in terms of the generator compliance threshold which is commonly used in industry. Results depend upon the information available for system identification, state estimation, and control. Depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all.
This study evaluated the equivalence of activity outcomes from three accelerometer brands worn on both wrists during free living. Forty-four adults wore a GENEActiv, ActiGraph and Axivity ...accelerometer for 7 days. Outcomes were assessed between and within accelerometer brand and wrist location with average acceleration and the intensity gradient (IG) being of particular interest. Pairwise 95% equivalence tests and intra-class correlation coefficients (ICC) evaluated agreement. Average acceleration and the IG were largely equivalent between combinations of accelerometer device and wrists when applying a 10% equivalence zone. There was largely a lack of equivalence between pairings for time spent in acceleration values ≥100 mg. However, equivalence was largely achieved when applying an equivalence zone that encompassed values ranging from 0.3 to 0.45 SDs for IG and time spent above 100 mg and 150 mg. Agreement between pairings tended to be stronger between different brands on the non-dominant (ICCs ≥ 0.73-0.97) versus the dominant wrist (ICCs ≥ 0.57-0.97) and between wrists for the same accelerometer (ICCs ≥ 0.59-0.97) for average acceleration and the IG. These are important findings since device placement is not consistent in studies. Further work that applies an equivalence zone reflecting the variability of the outcome measure is encouraged.
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