A Privacy-Masking Learning Algorithm for Online Distributed Optimization over Time-Varying Unbalanced Digraphs

This paper investigates a constrained distributed optimization problem enabled by differential privacy where the underlying network is time-changing with unbalanced digraphs. To solve such a problem, we first propose a differentially private online distributed algorithm by injecting adaptively adjustable Laplace noises. The proposed algorithm can not only protect the privacy of participants without compromising a trusted third party, but also be implemented on more general time-varying unbalanced digraphs. Under mild conditions, we then show that the proposed algorithm can achieve a sublinear expected bound of regret for general local convex objective functions. The result shows that there is a trade-off between the optimization accuracy and privacy level. Finally, numerical simulations are conducted to validate the efficiency of the proposed algorithm.

Distinguished Property in Tensor Products and Weak* Dual Spaces

A local convex space E is said to be distinguished if its strong dual Eβ′ has the topology β(E′,(Eβ′)′), i.e., if Eβ′ is barrelled. The distinguished property of the local convex space CpX of real-valued functions on a Tychonoff space X, equipped with the pointwise topology on X, has recently aroused great interest among analysts and Cp-theorists, obtaining very interesting properties and nice characterizations. For instance, it has recently been obtained that a space CpX is distinguished if and only if any function f∈RX belongs to the pointwise closure of a pointwise bounded set in CX. The extensively studied distinguished properties in the injective tensor products CpX⊗εE and in Cp(X,E) contrasts with the few distinguished properties of injective tensor products related to the dual space LpX of CpX endowed with the weak* topology, as well as to the weak* dual of Cp(X,E). To partially fill this gap, some distinguished properties in the injective tensor product space LpX⊗εE are presented and a characterization of the distinguished property of the weak* dual of Cp(X,E) for wide classes of spaces X and E is provided.

On the Accelerated Convergence of the Decentralized Event-triggered Algorithm for Convex Optimization

This article considers a problem of solving the optimal solution of the sum of locally convex cost functions over an undirected network. Each local convex cost function in the network is accessed only by each unit. To be able to reduce the amount of computation and get the desired result in an accelerated way, we put forward a fresh accelerated decentralized event-triggered algorithm, named as A-DETA, for the optimization problem. A-DETA combines gradient tracking and two momentum accelerated terms, adopts nonuniform step-sizes and emphasizes that each unit interacts with neighboring units independently only at the sampling time triggered by the event. On the premise of assuming the smoothness and strong convexity of the cost function, it is proved that A-DETA can obtain the exact optimal solution linearly in the event of sufficiently small positive step-size and momentum coefficient. Moreover, an explicit linear convergence speed is definitely shown. Finally, extensive simulation example validates the usability of A-DETA.

Seasonal and daily shifts in behavior and resource selection: how a carnivore navigates costly landscapes

The dynamic environmental conditions in highly seasonal systems likely have a strong influence on how species use the landscape. Animals must balance seasonal and daily changes to landscape risk with the underlying resources provided by that landscape. One way to balance the seasonal and daily changes in the costs and benefits of a landscape is through behaviorally-explicit resource selection and temporal partitioning. Here, we test whether resource selection of coyotes (Canis latrans) in Cape Breton Highlands National Park, Nova Scotia, Canada is behaviorally-explicit and responsive to the daily and seasonal variation to presumed costs and benefits of moving on the landscape. We used GPS data and local convex hulls to estimate space use and Hidden Markov Models to estimate three types of movement behavior: encamped, foraging, and traveling. We then used integrated step-selection analysis to investigate behaviorally explicit resource selection across times of day (diurnal, crepuscular, and nocturnal) and season (snow-free and snow). We found that throughout the day and seasonally coyotes shifted foraging behavior and altered behavior and resource choices to avoid moving across what we could be a challenging landscape. These changes in behavior suggest that coyotes have a complex response to land cover, terrain, and linear corridors that are not only scale dependent but also vary by behavior, diel period, and season. By examining the resource selection across three axes (behavior, time of day, and season), we have a more nuanced understanding of how a predator balances the cost and benefits of a stochastic environment.

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

This paper proposes a new mass estimation for a vehicle system, utilizing the characteristics of engine torque local convex minimum, where the mass can be estimated based on the driving forces and the longitudinal accelerations only. Fundamentally, this approach generally requires no other information about an aerodynamic effect, a road grade, or a rolling friction, which is usually demanded by the existing well-known longitudinal dynamics and adaptive filter-based estimation methods. The effectiveness of the proposed approach was evaluated and validated by both TruckSim/Simulink co-simulation and actual field test data. It is found that the proposed estimation technique is more favorable for a situation where the vehicle is exposed to low-speed regions. In addition to this new mass estimation strategy, other new and current existing methods were explored and are reviewed here. Moreover, this study suggested a guideline for a hybrid-type mass estimation strategy to predict a mass by combining a new method with an existing one for every speed.

A Model for Animal Home Range Estimation Based on the Active Learning Method

Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range estimator (DFHRE) is proposed in this study, based on the active learning method (ALM). The Euclidean distance is replaced by the cost distance-induced geodesic distance transformation to account for the effects of terrain and obstacles. Three datasets are used to verify the proposed method, and comparisons with the kernel density-based estimator (KDE) and the local convex hulls (LoCoH) estimators and the cross validation test indicate that the proposed estimator outperforms the KDE and the LoCoH estimators.

Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in Turbo-Generator Rotor Windings

Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil method. Compared with dynamic eccentricity faults, the characteristics of the variations in the main magnetic field after a turn-to-turn short circuit in rotor windings were analyzed and the unique characteristics were extracted. We propose that the degree of a turn-to-turn short circuit can be judged by the difference in the induction voltage of the double U-shaped detection coils mounted on the stator core. Here, the faulty slot position was determined by the local convex point formed by the difference in the induced voltage. Numerical simulation was used here to determine the induced voltage characteristics in the double U-shaped coils caused by the turn-to-turn short circuit fault. We analyzed the dynamic eccentricity fault as well as combined the fault of a turn-to-turn short circuit and dynamic eccentricity. Finally, we demonstrate the positive anti-interference performance associated with this fault detection method. This new online detection method is satisfactory in terms of sensitivity, speed, and positioning, and overall performance is superior to the traditional online detection methods.