A Forward-Backward-Forward Algorithm for Solving Quasimonotone Variational Inequalities

In this paper, we continue to investigate the convergence analysis of Tseng-type forward-backward-forward algorithms for solving quasimonotone variational inequalities in Hilbert spaces. We use a self-adaptive technique to update the step sizes without prior knowledge of the Lipschitz constant of quasimonotone operators. Furthermore, we weaken the sequential weak continuity of quasimonotone operators to a weaker condition. Under some mild assumptions, we prove that Tseng-type forward-backward-forward algorithm converges weakly to a solution of quasimonotone variational inequalities.

Self-Adaptive Rainfall Optimization-Based Reconfiguration of Distribution Networks

Reconfiguration that alters on/off status of sectionalizing and tie switches attempts to improve the operational performances of distribution networks (DNWs). This paper formulates the reconfiguration problem as a multi-objective optimization problem of reducing the net feeder loss(NFL), improving the node voltage profile (NVP) and enhancing the node voltage stability (NVS) and proposes a new reconfiguration method involving rainfall optimization (RO) for obtaining robust solutions. The method performs search for optimal tie switches in several sets of sectionalizing switches; each set is obtained from a closed loop formed for each tie switch, with a view of reducing the computational burden and adapting a self-adaptive technique for tuning the RO parameters for improving convergence. It performs study on 33-, 69- and 119-node DNWs and exhibits its superior performance over existing methods.

High-Performance Data Compression-Based Design for Dynamic IoT Security Systems

IoT technology is evolving at a quick pace and is becoming an important part of everyday life. Consequently, IoT systems hold large amounts of data related to the user of the system that is vulnerable to security breaches. Thus, data collected by IoT systems need to be secured efficiently without affecting the IoT systems’ performance and without compromising security as well. In this paper, a high-performance dynamic security system is introduced. The system makes use of the ZedBoard’s dynamic partial reconfiguration capability to shift between three distinct cipher algorithms: AEGIS, ASCON, and DEOXYS-II. The switching between the three algorithms is performed using two different techniques: the algorithm hopping technique or the power adaptive technique. The choice of which technique to be used is dependent on whether the system needs to be focused on performance or power saving. The ciphers used are the CAESAR competition finalists that achieved the greatest results in each of the three competition categories, where each cipher algorithm has its own set of significant characteristics. The proposed design seeks to reduce the FPGA reconfiguration time by the application of LZ4 (Lempel-Ziv4) compression and decompression techniques on the ciphers’ bitstream files. The reconfiguration time decreased by a minimum of 38% in comparison to the state-of-the-art design, while the resource utilization increased by approximately 2%.

Article Forecasts of Opportunity for Northern California Soil Moisture

Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences, yet systematic studies of soil moisture predictability are limited. Here, we use a data-adaptive technique, Linear Inverse Modeling, which has proved useful as an indication of predictability in other fields, to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1–2 weeks. An important advantage of this model is the a priori identification of forecasts of opportunity—conditions under which the model’s forecasts may be expected to have particularly high skill. Given that forecast errors (and inversely, their skill) can be estimated in advance, these findings have the potential to greatly increase the utility of soil moisture forecasts for practical applications including drought and flood forecasting.

Optimizing One-time and Continuous Subgraph Queries using Worst-case Optimal Joins

We study the problem of optimizing one-time and continuous subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We make two main contributions: 1. A cost-based dynamic programming optimizer for one-time queries that (i) picks efficient query vertex orderings for worst-case optimal plans and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. In addition to our optimizer, we describe an adaptive technique that changes the query vertex orderings of the worst-case optimal subplans during query execution for more efficient query evaluation. The plan space of our one-time optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. 2. A cost-based greedy optimizer for continuous queries that builds on the delta subgraph query framework. Given a set of continuous queries, our optimizer decomposes these queries into multiple delta subgraph queries, picks a plan for each delta query, and generates a single combined plan that evaluates all of the queries. Our combined plans share computations across operators of the plans for the delta queries if the operators perform the same intersections. To increase the amount of computation shared, we describe an additional optimization that shares partial intersections across operators. Our optimizers use a new cost metric for worst-case optimal plans called intersection-cost . When generating hybrid plans, our dynamic programming optimizer for one-time queries combines intersection-cost with the cost of binary joins. We demonstrate the effectiveness of our plans, adaptive technique, and partial intersection sharing optimization through extensive experiments. Our optimizers are integrated into GraphflowDB.

Adaptive Fast Nonsingular Fixed-Time Tracking Control for Robot Manipulators

This paper studies the fixed-time trajectory tracking control problem of robot manipulators in the presence of uncertain dynamics and external disturbances. First, a novel nonsingular fixed-time sliding mode surface is presented, which can ensure that the convergence time of the suggested surface is bounded regardless of the initial states. Subsequently, a novel fast nonsingular fixed-time sliding mode control (NFNFSMC) is developed so that the closed-loop system is fixed-time convergent to the equilibrium. By applying the proposed NFNFSMC method and the adaptive technique, a novel adaptive nonsingular fixed-time control scheme is proposed, which can guarantee fast fixed-time convergence of the tracking errors to small regions around the origin. With the proposed control method, the lumped disturbance is compensated by the adaptive technique, whose prior information about the upper bound is not needed. The fixed-time stability of the trajectory tracking control under the proposed controller is proved by the Lyapunov stability theory. Finally, corresponding simulations are given to illustrate the validity and superiority of the proposed control approach.