Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Analysis for Joint Delay-Power Tradeoff with Buffer/Channel-Aware and Its FPGA Implementation in Wireless Sensor Networks

In this paper, we aim to investigate the delay-power tradeoff problem which is attracting widespread interest due to its importance in wireless technology. This research has two main objectives. First, to assess the effect of different system parameters on the performance metrics. Second, to provide a solution for this optimization problem. A two-state, slow-fading channel is categorized into good and bad channel states. An adaptive transmission and random data arrivals are considered in our model. Each channel category has its own Markov chain, which is used in modeling the system. A joint Buffer-Aware and Channel-Aware (BACA) problem was introduced. In addition, an enhanced iterative algorithm was introduced for obtaining a sub-optimal delay-power tradeoff. The results show that the tradeoff curve is piecewise linear, convex and decreasing. Furthermore, a channel-aware system was investigated to provide analysis of the effect of system parameters on the delay and power. The obtained results show that the dominant factors that control the system performance are based on the arrival rate and the channel goodness factor. Moreover, a simplified field programable gate array (FPGA) hardware implementation for the channel aware system scheduler is presented. The implementation results show that the consumed power for the proposed scheduler is 98.5 mW and the maximum processing clock speed is 190 MHz.

Passive, Transitioning Mounts for Simultaneous Shock and Vibration Isolation

A design strategy to simultaneously mitigate the effects of both shock and vibration is introduced. The proposed isolation mount is a passive, transitioning mount and consists of sliding friction elements in series connection with springs and dampers. A linear and a displacement dependent viscous damper are considered, while linear, hardening and softening springs, are considered. The isolation mount’s response is determined by numerical simulation. For a single-degree-of-freedom system, the tradeoff curve for a half-sine velocity input is determined, as is the nonlinear transmissibility for harmonic excitation. The method is found to achieve satisfactory isolation against shock events as well as persistent harmonic inputs. The suggested mount configuration was also found to have good performance against a ‘combined’ input with both resonant and transient content.

Precise Undersampling Theorems

Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibitphase transitionsin performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k-sparse signal of length N from n measurements, provided n ?? 2k ?? log(N/n), for (k,n,N) large.k ?? N.AMS 2000 subject classifications. Primary: 41A46, 52A22, 52B05, 62E20, 68P30, 94A20; Secondary: 15A52, 60F10, 68P25, 90C25, 94B20.

An Efficient Tradeoff Approach for Topology Optimization With Manufacturing Constraints

In this paper an efficient approach for generating tradeoff curves when performing topology optimization with manufacturing constraints is presented. By minimizing a new stiffness-volume ratio, or in-fact a new compliance-volume product, the tradeoff curve is generated by changing a new design parameter. The volume appearing in the objective is raised to the power of this new design parameter. In such manner different conceptual designs can be generated. By adopting a nested approach, the problem is easily solved by a simple numerical scheme. This is a nice feature of the approach which makes the numerical performance most efficient and robust. This feature makes it also easy to include manufacturing constraints by simply updating the move limits such that these constraints are satisfied. The design parametrization is done by the SIMP-model and patterns of checker-boards are prevented by adopting Sigmund’s filter. The efficiency of the approach is demonstrated by presenting tradeoff curves for both 2D- and 3D-problems.

Instantaneous Optimal Control of Seismically-Excited Structures Using a Maximum Principle

An optimal control scheme is proposed for linear dynamic system models, and the optimal control forces are derived using a maximum principle. Numerical examples are presented demonstrating the characteristics of the control scheme. An instantaneous extension to the optimal control method is proposed to deal with the unknown nature of seismic disturbances. Examples of the effectiveness of the method and a tradeoff curve are presented.

An overview of tradeoff curve analysis in the design of manufacturing systems

Uncertainty in manufacturing systems has long been the source of managerial complexity. In this paper we discuss the impact of different sources of uncertainty and present a methodology to assess their impact on system behavior. We introduce the concept of tradeoff curves as a characteristic of a manufacturing system and illustrate their use to make decisions concerning the amount and type of capacity necessary to manage the system efficiently, to assess the impact of products arrival and processing uncertainties, as well as the consequences of changes in throughput and product mix. The methodology is illustrated with an example derived from an actual application in the semiconductor industry.