Existing Optimization Schemes Research Articles

Robust optimization of hypoid gear contact performance considering tooth form error: Design sensitivity and Pareto front

Optimization of hypoid gear contact performance is challenging due to its nonconvex nature and the handling of non-linear constraints involving complex gear contact behavior. In addition, the contact performance in the existing optimization schemes is highly reliant on the manufacturing quality, for example, the uncertain tooth form errors induced by machining tolerances and blade wear are usually ignored. To tackle these problems, a robust optimization methodology is proposed for hypoid gear contact performance. A finite element/contact mechanics model is established, which considers the stochastic tooth form errors following a Gaussian distribution model. The non-linear constraints are incorporated to avoid edge contact under loaded conditions, and tooth surface interference due to error. This methodology guarantees the feasible solution space and reduces the optimization complexity by the independent identification model. The identification model of the optimal solution is flexible in the parameter selection while keeping tooth depth. This non-deterministic problem is optimized based on the pattern search method. The Pareto front and design sensitivity analysis demonstrate the effectiveness and robustness of the proposed optimization methodology.

Reducing Losses and Energy Storage Requirements of Modular Multilevel Converters With Optimal Harmonic Injection

Due to the single phase characteristic of the individual arms of the Modular Multilevel Converter (MMC) topology, the difference between the instantaneous AC and DC side power must be buffered in the module capacitors. This results in large module capacitors compromising the power density and cost of the MMC. In this paper, a multi-objective optimization scheme is formulated that aims at reducing the required module capacitance and the semiconductor losses at the same time. Further attention is paid to the maximum AC voltage amplitude or the maximum current. The optimization scheme is based on the injection of circulating current and AC common mode voltage harmonics. Unlike most existing optimization schemes it considers the actual trajectories of capacitor voltage and arm output voltage to maximize the savings in module capacitance and semiconductor losses. For an exemplary medium voltage MMC parameter set, capacitance value reductions of more than 50% are achieved while the semiconductor losses decrease by 8- 18%. Based on a volume estimation for MMC modules, this results a volume reduction of up to 45%.

Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach

Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.

An Optimization of the Signal-to-Noise Ratio Distribution of an Indoor Visible Light Communication System Based on the Conventional Layout Model

For an actual visible light communication system, it is necessary to consider the uniformity of indoor illumination. Most of the existing optimization schemes, however, do not consider the effect of the first reflected light, and do not conform to the practical application conventions, which increases the actual cost and the complexity of system construction. In this paper, considering the first reflected light and based on the conventional layout model and the classic indoor visible light communication model, a scheme using the parameter Q to determine the optimal layout of channel quality is proposed. We determined the layout, and then carried out a simulation. For comparison, the normal layout and the optimal layout of illumination were also simulated. The simulation results show that the illuminance distributions of the three layouts meet the standards of the International Organization for Standardization. The optimal layout of channel quality in the signal-to-noise ratio distribution, maximum delay spread distribution, and impulse response is obviously better than the optimal layout of illumination. In particular, the effective area percentage of the optimal layout of channel quality is increased by 0.32% and 6.08% to 88.80% as compared with the normal layout’s 88.48% and the optimal layout of illumination’s 82.72%. However, compared with the normal layout, the advantages are not very prominent.

Modeling & Control of Human Actuated Systems

Abstract This paper investigates a Cyber-Physical & Human System (CPHS) comprised of a deterministic dynamical system plant model and a human actuator model. Namely, human decisions are stochastic inputs to the plant model. We examine a framework where human decisions cannot be directly controlled, but can be influenced via incentive control signals. Specifically, we use the framework of discrete choice models (DCMs) to capture human decision making, and then design optimal controllers for these human actuated dynamical systems. Existing literature on CPHS often treats human inputs as stochastic and exogenous inputs, and then formulates a disturbance rejection problem. Instead of treating human decision-making as an uncontrollable exogenous input, we directly incorporate human decision making into the modeling framework with DCMs. This paper thus adds two original contributions. (i) We develop a generalized human-actuated system framework based on DCM that predicts the probability of human decisions, conditioned on controllable incentives. (ii) We show that existing optimization schemes, such as Sequential Quadratic Programming (SQP) and Dynamic Programming (DP), can be applied to control the proposed human-actuated system. We conclude this paper by demonstrating the framework on a reference tracking problem and an inventory control problem.

Cross-layer design benchmark for throughput maximization with fairness and delay constraints in DCF systems

This paper proposes a cross-layer design for the stations (STAs) in a distributed coordination function (DCF) network. By selecting the modulation scheme, coding scheme, and packet length of each STA in the network, the design aims to maximize the total throughput of all the STAs and satisfy the minimum throughput requirement or delay requirement of each STA, thus addressing the fairness and delay issues. The proposed scheme applies to the system where each STA employs a contention based channel access mechanism, Furthermore, unlike the existing optimization schemes, it takes two important factors, changeable data rate and changeable packet error rate (PER), into consideration. Using an existing Markov chain model to predict the performance of the STAs, we propose an approach that updates the selection of each STA sequentially, thus avoiding the large complexity from the exhaustive search. Many issues are discussed based on the numerical results, including how the approximations in our design affect the processing time and result of the design, how the change of one STA affects the performance of the other STAs, how the minimum throughput constraints affect the fairness and total throughput, how to select these constraints to satisfy the delay requirements, etc.

Joint Dynamic Rate Control and Transmission Scheduling for Scalable Video Multirate Multicast Over Wireless Networks

In this paper we consider the time-varying characteristics of practical wireless networks and propose a joint dynamic rate allocation and transmission scheduling optimization scheme for scalable video multirate multicast based on opportunistic routing (OR) and network coding. With OR the decision of optimal routes for scalable video coding layered streaming is integrated into the joint optimization formulation. The network throughput is also increased by taking advantage of the broadcast nature of the wireless shared medium and by network coding operations in intermediate nodes. To maximize the overall video reception quality among all destinations the proposed scheme can jointly optimize the video reception rate the associated routes to different destinations and the time fraction scheduling of transmitter sets that are concurrently transmitting in the shared wireless medium. By using dual decomposition and primal-dual update approach we develop a cross-layer algorithm in a fully distributed manner. Simulation results demonstrate significant network multicast throughput improvement and adaptation to dynamic network changes relative to existing optimization schemes.

Word- and Partition-Level Write Variation Reduction for Improving Non-Volatile Cache Lifetime

Non-volatile memory technologies are among the most promising technologies for implementing the main memories and caches in future microprocessors and replacing the traditional DRAM and SRAM technologies. However, one of the most challenging design issues of the non-volatile memory technologies is the limited write. In this article, we first propose to exploit the narrow-width values to improve the lifetime of non-volatile last-level caches with word-level write variation reduction. Leading zeros masking scheme is proposed to reduce the write stress to the upper half of the narrow-width data. To balance the write variations between the upper half and the lower half of the narrow-width data, two swapping schemes, the swap on write (SW) and swap on replacement (SRepl), are proposed. Two existing optimization schemes, the multiple dirty bit (MDB) and read before write (RBW), are adopted with our word-level swapping design. To further reduce the write variation on the partition level, we propose to exploit the cache partitioning design to improve the lifetime. Based on the observation that different applications demonstrate different cache access (write) behaviors, we propose to partition the last-level cache for different applications and balance the write variations by partition swapping. Both software-based and hardware-based partitioning and swapping schemes are proposed and evaluated for different situations. Our experimental results show that both our word- and partition-level designs can improve the lifetime of the non-volatile caches effectively with low performance and energy overheads.

An algorithm for LQ optimal actuator location

The locations of the control hardware are typically a design variable in controller design for distributed parameter systems. In order to obtain the most efficient control system, the locations of control hardware as well as the feedback gain should be optimized. These optimization problems are generally non-convex. In addition, the models for these systems typically have a large number of degrees of freedom. Consequently, existing optimization schemes for optimal actuator placement may be inaccurate or computationally impractical. In this paper, the feedback control is chosen to be an optimal linear quadratic regulator. The optimal actuator location problem is reformulated as a convex optimization problem. A subgradient-based optimization scheme which leads to the global solution of the problem is used to optimize actuator locations. The optimization algorithm is applied to optimize the placement of piezoelectric actuators in vibration control of flexible structures. This method is compared with a genetic algorithm, and is observed to be faster and more accurate. Experiments are performed to verify the efficacy of optimal actuator placement.

Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks

This paper proposes a distributed robust optimization scheme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over practical wireless networks. In order to guarantee layered utility maximization, the initial nominal joint source and network optimization is defined, where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates with network coding. To enhance the robustness of the nominal convex optimization formulation with nonlinear constraints, we reserve partial bandwidth for backup paths disjoint from the primal paths. It considers the path-overlapping allocation of backup paths for different receivers to take advantage of network coding, and takes into account the robust multipath rate-control and bandwidth reservation problem for scalable video multicast streaming when possible link failures of primary paths exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters related to the wireless channel. The targeted uncertainty in the robust optimization problem is studied in a form of protection functions with nonlinear constraints, to analyze the tradeoff between robustness and distributedness. Using the dual decomposition and primal-dual update approach, we develop a fully decentralized algorithm with regard to communication overhead. Through extensive experimental results under critical performance factors, the proposed algorithm could converge to the optimal steady-state more quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes.

Resource Allocation via Linear Programming for Fractional Cooperation

In this letter, resource allocation is considered for large multi-source, multi-relay networks employing fractional cooperation, in which each potential relay only allocates a fraction of its resources to relaying. Using a Gaussian approximation, it is shown that the optimization can be posed as a linear program, where the relays use a demodulate-and-forward (DemF) strategy, and where the transmissions are protected by low-density parity-check (LDPC) codes. This is useful since existing optimization schemes for this problem are nonconvex.

Optimizing Instruction Scheduling through Combined In-Order and O-O-O Execution in SMT Processors

The resource sharing nature of Simultaneous Multithreading (SMT) processors and the presence of long latency instructions from concurrent threads make the instruction scheduling window (IW), which is a primary shared component among key pipeline structures in SMT, a performance bottleneck. Due to the tight constraints on its physical size, the IW faces more severe pressure to handle the instructions from various threads while attempting to avoid resource monopolization by some low-ILP threads. It is particularly challenging to optimize the efficiency and fairness in IW utilization to fulfill the affordable performance by SMT under the shadow of long latency instructions. Most of the existing optimization schemes in SMT processors rely on the fetch policy to control the instructions that are allowed to enter the pipeline, while little effort is put to control the long latency instructions that are already located in the IW. In this paper, we propose streamline buffers to handle the long latency instructions that have already entered the pipeline and clog the IW, while the controlling fetch policies take time to react. Each streamline buffer extracts from IW and holds a chain of instructions from a thread that are stalled by dependency on a long latency load. When the load value returns, the streamline buffer then serves these instructions directly to in-order execution, avoiding any instruction replay. This is done in supplement to the conventional IW that serves in parallel the other instructions for out-of-order (o-o-o) execution. Analysis of SPEC2000 integer and FP benchmarks reveals that instructions dependent on long latency loads, typically have their first source operand ready within 5 percent-15 percent of their total wait time in the IW. Our scheme is able to utilize this asymmetry in source operands' ready time to achieve a complexity effective design. As compared to the baseline SMT architecture, our design when working in conjunction with earlier proposed ICOUNT.2.8 fetch policy for 4-threads effectively reduces the IW full rate by 9.4 percent (11 percent for 2-thread), improves average IPC for MIXED workloads by 9.6 percent (8 percent for MEM workloads and 4.4 percent for CPU workloads), and fairness by 7.56 percent (7.24 percent for 2-thread). Similar enhancements are observed when run in conjunction with an RR.2.8 fetch policy. Further, our scheme when combined with DCRA improves the performance on the average by 21.7 percent, while DCRA improves by 16.3 percent when run alone.

A multipass optimization strategy for CNC lathe operations

The optimization of cutting conditions has been and still is an active area of research since Gilbert's (1950) first work in machining economics. Many concepts and optimization procedures have been developed since then to obtain optimum conditions, taking into account as many influencing variables as possible. However, the use of these techniques in machinability data base systems, NC part programming systems and process planning systems so far has been very limited, even though there is an urgent need for such a technique. Existing optimization schemes do not necessarily give optimum machining solutions. Several works reported in the literature restricted multi-pass problems to two-pass problem so as to simplify the solution method or to treat them as single-pass problems. This paper describes the development of a methodology for an effective optimization of machining conditions of multi-pass lathe operations. The objective is to use the optimal even depth of cut strategy to simplify the solution method that determines near-optimum and practical solutions. The solutions obtained can be conveniently implemented in the part programmes of a CNC lathe. Such work can provide a wider acceptance and more practical application of machining economics in industry.

Solving stochastic optimization models with learning and rational expectations

In this paper we present a general single-agent stochastic dynamic optimization model that is capable of dealing with learning and with rational expectations. We obtain the solution by solving at every time step the rational expectations model and then applying existing optimization schemes to derive the optimal solution.