Approaching Prosumer Social Optimum via Energy Sharing with Proof of Convergence

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers’ strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers’ strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

Approaching Prosumer Social Optimum via Energy Sharing with Proof of Convergence

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers' strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers' strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results.Comment: 15 pages, 8 figures; accepted by IEEE Transactions on Smart Gri

Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples. These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world. We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., \emph{inter-class loss distribution discrepancy} and \emph{misleading predictions due to uncertainty}. The first issue is that existing methods often perform class-agnostic noise modeling. However, loss distributions show a significant discrepancy among classes under class imbalance, and class-agnostic noise modeling can easily get confused with noisy samples and samples in minority classes. The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples. Inspired by our observations, we propose an Uncertainty-aware Label Correction framework~(ULC) to handle label noise on imbalanced datasets. First, we perform epistemic uncertainty-aware class-specific noise modeling to identify trustworthy clean samples and refine/discard highly confident true/corrupted labels. Then, we introduce aleatoric uncertainty in the subsequent learning process to prevent noise accumulation in the label noise modeling process. We conduct experiments on several synthetic and real-world datasets. The results demonstrate the effectiveness of the proposed method, especially on imbalanced datasets

Distributed Frequency Control with Operational Constraints, Part I: Per-Node Power Balance

This paper addresses the distributed optimal frequency control of multi-area power system with operational constraints, including the regulation capacity of individual control area and the power limits on tie-lines. Both generators and controllable loads are utilized to recover nominal frequencies while minimizing regulation cost. We study two control modes: 1) the per-node balance mode and 2) the network balance mode. In Part I of this paper, we only consider the per-node balance case, where we derive a completely decentralized strategy without the need for communication between control areas. It can adapt to unknown load disturbance. The tie-line powers are restored after load disturbance, while the regulation capacity constraints are satisfied both at equilibrium and during transient. We show that the closed-loop systems with the proposed control strategies carry out primal-dual updates for solving the associated centralized frequency optimization problems. We further prove the closed-loop systems are asymptotically stable and converge to the unique optimal solution of the centralized frequency optimization problems and their duals. Finally, we present simulation results to demonstrate the effectiveness of our design. In Part II of this paper, we address the network power balance case, where transmission congestions are managed continuously

Clinical efficacy and drug resistance of ceftazidime-avibactam in the treatment of Carbapenem-resistant gram-negative bacilli infection

ObjectiveTo examine the clinical efficacy, safety, and resistance of Ceftazidime-Avibactam (CAZ-AVI) in patients with Carbapenem-resistant Gram-negative bacilli (CR-GNB) infections.MethodsWe retrospectively analyzed relevant data of CR-GNB infected patients receiving CAZ-AVI treatment, analyzed relevant factors affecting drug efficacy, and compared the efficacy and safety with patients receiving Polymyxin B treatment.ResultsA total of 139 patients were included. Agranulocytosis, septic shock, SOFA score, and CAZ-AVI treatment course were independent risk factors affecting the prognosis of patients with CR-GNB infection treated with CAZ-AVI while prolonging the treatment course of CAZ-AVI was the only protective factor for bacterial clearance. The fundamental indicators showed no statistically significant differences between CAZ-AVI and Polymyxin B treatment groups. At the same time, the proportion of patients treated with monotherapy was significantly higher in the CAZ-AVI group than in the Polymyxin B group (37.2% vs. 8.9%, p &lt; 0.05), the 30-day mortality rate of the CAZ-AVI treatment group (27.7% vs. 46.7%, p = 0.027) was lower than that of the Polymyxin B treatment group. The 30-day clinical cure rate (59.6% vs. 40% p = 0.030) and 14-day microbiological clearance rate (42.6% vs. 24.4%, p = 0.038) were significantly higher in the CAZ-AVI than in the Polymyxin B treatment group. Eighty nine patients were monitored for CAZ-AVI resistance, and the total resistance rate was 14.6% (13/89). The resistance rates of Carbapenem-resistant Klebsiella pneumoniae (CRKP) and Carbapenem-resistant Pseudomonas aeruginosa (CRPA) to CAZ-AVI were 13.5 and 15.4%, respectively.ConclusionCAZ-AVI has shown high clinical efficacy and bacterial clearance in treating CR-GNB infections. Compared with Polymyxin B, CAZ-AVI significantly improved the outcome of mechanical ventilation in patients with septic shock, agranulocytosis, Intensive Care Unit (ICU) patients, bloodstream infection, and patients with SOFA score &gt; 6, and had a lower incidence of adverse events. We monitored the emergence of CAZ-AVI resistance and should strengthen the monitoring of drug susceptibility in clinical practice and the rational selection of antibiotic regimens to delay the onset of resistance