Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ***:The included cases were...
详细信息
Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ***:The included cases were composed of the suspectedpertussis patients who came from Mazandaran province andregistered in the center for Disease control and Prevention from20 March 2012 to 20 March 2018.A discrete wavelet transformbasedmethod was used to detect the *** analyseswere performed using MATLAB Software version 2018a andExcel ***:A total of 1162 cases were recruited in the study,including 545(46.90%)males and 617(53.10%)females,withmedian age of 1.47(0.22-9.56)*** median age of maleswas 1.18(0.21-8.24)years,while that of females was 1.82(0.21-10.75)*** the level of the alarm threshold,it was1.28 case/d when k=2,while it was 1.34 case/d when k=*** detected aberration days were 123 d and 57 d by consideringk=2 and 3,*** most defined alarm threshold wasrelated to spring(>2 cases/d)and summer(>1 case/d),***:The sensitivity of the surveillance system issubjected to a different ***,determining the level of alarmthreshold periodically using different methods is recommended.
This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrari...
详细信息
This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrarily across iterations, and the information of them is privately revealed to each agent at each iteration. For a uniformly jointly strongly connected time-varying directed graph, we propose two distributed bandit online primal–dual algorithm with compressed communication to efficiently utilize communication resources in the one-point and two-point bandit feedback settings, respectively. In nonconvex optimization, finding a globally optimal decision is often NP-hard. As a result, the standard regret metric used in online convex optimization becomes inapplicable. To measure the performance of the proposed algorithms, we use a network regret metric grounded in the first-order optimality condition associated with the variational inequality. We show that the compressed algorithm with one-point bandit feedback establishes an O(Tθ1) network regret bound and an O(T7/4−θ1) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (3/4,5/6] is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T5/2−2θ1). In addition, we show that the compressed algorithm with two-point bandit feedback establishes an O(Tmax{1−θ1,θ1}) network regret and an O(T1−θ1/2) network cumulative constraint violation bounds, where θ1 ∈ (0,1). Moreover, the network cumulative constraint violation bound is reduced to O(T1−θ1) under Slater’s condition. The bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with inequality constraints. To the best of our knowledge, thi
Twin-field (TF) quantum key distribution (QKD) is highly attractive because it can beat the fundamental limit of secret key rate for point-to-point QKD without quantum repeaters. Many theoretical and experimental stud...
详细信息
In recent work, Akers et al. proved that the entanglement of purification Ep(A : B) is bounded below by half of the q-Rényi reflected entropy SR(q)(A : B) for all q ≥ 2, showing that Ep(A : B) = 1/2 SR(q)(A : B)...
详细信息
Chirality, nonreciprocity, and quantum correlations are at the center of a wide range of intriguing effects and applications across natural sciences and emerging quantum technologies. However, the direct link combinin...
详细信息
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet...
详细信息
Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking two questions: 1. which classification loss provides more generalization for feature learning in OSV?, and 2. How integration of different losses into a unified multi-loss function lead to an improved learning framework? These questions are studied based on analysis of three loss functions, including cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to complementary features of these losses, we combine them into a dynamic multi-loss function and propose a novel ensemble framework for simultaneous use of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of several sequential trials. In each trial, a dominant loss function is selected from the multi-loss set, and the remaining losses act as a regularizer. Different trials learn diverse representations for each input based on signature identification task. This multi-representation set is then employed for the verification task. An ensemble of SVMs is trained on these representations, and their decisions are finally combined according to the selection of most generalizable SVM for each user. We conducted two sets of experiments based on two different protocols of OSV, i.e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG. Based on the writer-dependent OSV protocol, On UT-SIG, we achieved 6.17% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 9.61%. Our method surpassed state-of-the-arts by 2.5% on GPDS-Synthetic, achieving 6.13%. Our result on MCYT was also comparable to the best previous results. The
Quantum annealing is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with a...
详细信息
Due to the nonlinearity of dynamical equations in robotic systems, nonlinear compliance has more potential for energy consumption reduction. Moreover, the legged robots require adaptive structures so as to maximize th...
详细信息
ISBN:
(数字)9781728166049
ISBN:
(纸本)9781728166056
Due to the nonlinearity of dynamical equations in robotic systems, nonlinear compliance has more potential for energy consumption reduction. Moreover, the legged robots require adaptive structures so as to maximize their efficiency w.r.t. the new environments and gaits. Therefore, having nonlinear adaptive compliance is essential in legged robots. In this paper, we take one step towards the realization of nonlinear compliances with adaptable torque-deflection profiles. So as to achieve this goal, we present a mechanism which consists of four linear extension springs. Changing the arrangement of linear springs can adapt the compliance profile to the desired one. In addition, to optimize the arrangement of linear springs in an online manner, an adaptation rule is presented. The proposed adaptation rule adapts the compliance profile such that the actuator's torque and consequently the energy consumption are reduced. The performance of presented adaptation rule for actuator's torque minimization is analyzed by means of simulations. In simulations, we observed that the adaptation rule not only minimizes the energy consumption but also improves the tracking performance of the controller. Finally, to realize nonlinear adaptive compliance, a prototype of the presented mechanism is designed and constructed.
Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized ene...
详细信息
Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximizatio...
详细信息
暂无评论