Framework For Performance Optimization Research Articles

Optimization of Linearized Belief Propagation for Distributed Detection

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.

Mode Selection and Spectrum Partition for D2D Inband Communications: A Physical Layer Security Perspective

This paper investigates from the physical layer security (PLS) perspective the fundamental issues of mode selection and spectrum partition in cellular networks with inband device-to-device (D2D) communication. We consider a mode selection scheme allowing each D2D pair to probabilistically switch between the underlay and overlay modes, and also a spectrum partition scheme where the system spectrum is orthogonally partitioned between cellular and overlay D2D communications. We first develop a general theoretical framework to model both the secrecy outage/secrecy capacity performance of cellular users and outage/capacity performance of D2D pairs, and to conduct performance optimization to identify the optimal mode selection and spectrum partition for secrecy capacity maximization and secrecy outage probability minimization. A case study is then provided to demonstrate the application of our theoretical framework for performance modeling and optimization, and also to illustrate the impacts of mode selection and spectrum partition on the PLS performances of inband D2D communications.

A Parallelization and Performance Optimization Framework for Mesh-Based Simulations Using Target Execution Models

Mesh-based applications usually use very big mesh structures for simulation accuracy, and they are coded in different programming languages using different parallel runtime support systems. They run in various parallel computation systems. It is a big problem that in such a situation how can domain expert build a mesh-based application which well fit the different performance requirements for different target computation systems. Another problem is that it becomes harder to create well performed parallel programs for modern computing systems for domain experts even for computing specialists because modern computing systems become more complicated. In this paper an automatic parallelizing and performance optimizing framework is proposed and implemented for mesh-based application domains through a translation from domain description into internal programming model. Using internal programming models and their performance models with target system execution models, domain descriptions can be effectively parallelized and optimized into parallel code for target systems. Several CFD codes are tested in the framework as case-studies and experiments shown that more efficient codes, which are more scalable and of higher performance, can be generated for different target computation systems.

Robust Beamforming Techniques for Non-Orthogonal Multiple Access Systems with Bounded Channel Uncertainties

In this letter, we propose a robust beamforming design for non-orthogonal multiple access (NOMA) based multiple-input single-output (MISO) downlink systems. In particular, the robust power minimization problem is studied with imperfect channel state information (CSI), where the beamformers are designed by incorporating norm-bounded channel uncertainties to provide the required quality of service at each user. This robust scheme is developed based on the worst-case performance optimization framework. In terms of beamforming vectors, the original robust design is not convex and therefore, the robust beamformers cannot be obtained directly. To circumvent this non-convex issue, the original intractable problem is reformulated into a convex problem, where the non-convex constraint is converted into a linear matrix inequality (LMI) by exploiting S-Procedure. Finally, simulation results are provided to demonstrate the effectiveness of the proposed robust design.

A design synthesis framework for directional performance optimization of multi-trailer articulated heavy vehicles with trailer lateral dynamic control systems

This paper presents a design synthesis framework for directional performance optimization of multi-trailer articulated heavy vehicles with trailer lateral dynamic control systems, e.g. active trailer steering, trailer differential braking, active roll control or the coordination of the three systems. In order to demonstrate the effectiveness of the proposed framework, it was applied to the design optimization of a multi-trailer articulated heavy vehicle with an active trailer steering controller. In the design, a set of lateral stability measures is originally defined, and the design problem under simulated test manoeuvres is implemented using a parallel computing technique. It is illustrated that the proposed framework and the performance measures can be used to identify effectively the desired variables and to predict reliably the performance envelopes of multi-trailer articulated heavy vehicles with active safety systems by considering the concept of driver-adaptive-vehicle design.

Performance optimization of an aluminum factory in economic crisis by integrated resilience engineering and mathematical programming

Economic crisis have created certain challenges for large factories. This has intensified the need for being resilient in face of such disruption. During economic crisis, factories are faced with significant threats in their financial performance and survivorship. In this paper, the performance of an aluminum production plant has been investigated by integrated resilient engineering (IRE). For assessing IRE performance in economic crisis of the case of this study, first a questionnaire based on IRE factors in context of economic crisis is distributed among all expert managers and staff. Afterwards, the Cronbach’s alpha test is used to investigate the reliability of the questionnaire’s data. Then, best fitted data envelopment analysis (DEA) is used to examine resilience engineering factors in context of economic crisis performance. To validate and verify the results, principle component analysis (PCA) and numerical taxonomy (NT) are utilized. It is found that self-organization, reporting culture, flexibility and learning have the greatest effect on performance. Also, it is shown that integrated resilient engineering is more efficient than resilient engineering. It is proven that the aluminum factory can be more resilient in cases of economic crisis by improving the above factors. The framework of this study may be used for all types of industrial units to identify the most important resilient factors in times of economic crisis. This is the first study that presents an integrated framework for performance optimization of an aluminum factory in economic crisis.

Framework for advanced exergoeconomic performance analysis and optimization of an oil shale retorting process

A framework based on advanced exergoeconomic analysis is proposed for evaluation and optimization of oil shale retorting processes. The proposed approach aims to facilitate identification of the improvement potential of energy conversion systems in oil shale retorting. A Fushun-type OSR (oil shale retorting) process is analyzed to illustrate the application of the proposed framework. The results indicate that the total exergy destruction rate of the OSR process under consideration is 442.62 MW, of which 54.60% is avoidable. The total exergy cost and total avoidable cost of the OSR process are 323.08 × 106 CNY/y and 115.51 × 106 CNY/y, i.e., the improvement potential of the OSR process is 35.75%. The retort is found to be the component of the OSR process having the greatest potential for decrease of exergy destruction cost. Following optimization, the cost per exergy unit of product of the six components of the OSR process decreases by 6.93%–11.28%. The total cost per exergy unit of product is reduced by 5.62%. The total avoidable cost is reduced by 17.03% and the exergy efficiency increased by 2.41%.

Mobile Energy Sharing Networks: Performance Analysis and Optimization

Energy is a scarce resource in wireless communications and mobile networking. In this paper, a novel concept of mobile energy sharing networks is proposed to enhance the energy usage of wireless networks. In such networks, mobile users can share energy with one another when they move and meet, thus minimizing the chances of ending up with insufficient energy for their consumption. This concept is similar to the notion of opportunistic mobile social networks in which the users can share information when they travel and encounter each other. To optimize the operation of the proposed network, a performance analysis and optimization framework tailored toward mobile energy sharing networks is developed. This framework has two main components. The first component allows obtaining the optimal energy sharing policy between mobile users who agree to share energy (i.e., a pair of matched users). To obtain this optimal policy, a stochastic optimization problem is formulated while taking into account the mobility and energy levels of the users' batteries. The second component of the proposed framework is to determine stable user matching for energy sharing. The user will find another user with whom to share energy. The policy obtained from the first component is applied such that the matched users achieve the minimum energy outage probability. An extensive performance evaluation of the proposed mobile sharing networks and framework is conducted in order to highlight their potential benefits.

Passive performance and building form: An optimization framework for early-stage design support

To achieve low and zero net energy performance objectives in buildings, designers must make optimal use of passive environmental design strategies. The objective of this research is to demonstrate the application of a novel Passive Performance Optimization Framework (PPOF) to improve the performance of daylighting, solar control, and natural ventilation strategies in the early design stages of architectural projects. The PPOF is executed through a novel, simulation-based parametric modeling workflow capable of optimizing building geometry, building orientation, fenestration configurations, and other building parameters in response to program requirements, site-specific building adjacencies, and climate-based daylighting and whole-building energy use performance metrics. The applicability of the workflow is quantified by comparing results from the workflow to an ASHRAE 90.1 compliant reference model for four different climate zones, incorporating real sites and urban overshadowing conditions. Results show that the PPOF can deliver between a 4% and 17% reduction in Energy Use Intensity (EUI) while simultaneously improving daylighting performance by between 27% and 65% depending on the local site and climatic conditions. The PPOF and simulation-based workflow help to make generative modeling, informed by powerful energy and lighting simulation engines, more accessible to designers working on regular projects and schedules to create high-performance buildings.

BPOpt: A framework for BIM-based performance optimization

The increase in global environmental concerns as well as the advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This paper presents an integrated framework for building information modeling (BIM)-based performance optimization, BPOpt. This framework enables designers to explore design alternatives using an open-source, visual programming user interface on the top of a widely used BIM platform, to generate models of building design options, assess the environmental performance of the models through cloud-based simulation, and search for the most appropriate design alternatives. This paper details the process of the development of BPOpt and also provides a case study to show its application. The case study demonstrates the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building. Finally, strengths, limitations, current adoption by academia and industry, and future improvements of BPOpt for high-performance building design are discussed.

Cooperation in Delay-Tolerant Networks With Wireless Energy Transfer: Performance Analysis and Optimization

We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or to request wireless energy transfer. Second, multiple mobile nodes can cooperate and form coalitions to help one another in the delivery of packets from their associated data sources. However, this cooperation may not be always beneficial due to the limited buffer and energy resources. Moreover, some mobile nodes may secretly decide to deviate from a given coalition, thus taking advantage of the other innocent mobile nodes. To address these two issues, this paper introduces a performance analysis and optimization framework, which is based on a joint optimization and game-theoretic framework. The optimization model is used to obtain the packet delivery policy of each individual mobile node. Then, a novel game-theoretic model, namely a repeated coalition formation game , is developed to analyze the cooperation strategies of multiple mobile nodes.

스마트폰 응용 프로그램의 사용자 경험 향상을 위한 사용자 중심 반응 시간 분석 도구

We propose a novel user-perceived performance optimization framework for the Android platform that takes advantage of the user-centric response time analysis. To this end, we propose a new definition of response time, which we call the user-centric response time, as a metric for the quality of user-perceived performance of the smartphone application. In this paper, we describe the design and implementation of an on-line user-centric response time analyzer for Android-based smartphones, which provides smartphone application developers with valuable insight for user-perceived performance optimization. We implemented the user-centric response time analyzer on the Android platform, version 4.0.4 (ICS) running on a Galaxy Nexus smartphone. From our experimental results, the proposed user-centric response time analyzer accurately estimates user-centric response times with an accuracy of 92.0% compared to manually measured times with less than 1% performance penalty. In order to evaluate the efficiency of the proposed framework, we were able to reduce the user-centric response time of the target application by up to 16.4% based on the evaluation results by the proposed framework.

Routing, spectrum access, and scheduling in multi-hop multi-channel wireless networks with MIMO links

Software-defined radios (SDRs) are a promising technology to enable dynamic channel access and sharing. Multiple Input Multiple Output (MIMO) is another radio technology breakthrough for increasing wireless network capacity. To obtain the full benefits brought by the SDR and MIMO technologies in wireless mesh networks (WMNs), the higher layer mechanisms should exploit their capabilities in a systematic way. In this paper, we investigate routing, channel assignment, link scheduling, and MIMO transmission mode control in multi-channel multi-antenna wireless mesh networks. We first present a cross-layer framework for network performance optimization. Network traffic routing is established on a relatively long time scale to maintain system stability. We then proposed a stream controlled multiple access (SCMA) scheme, which is responsible for assigning frequency channels and scheduling links for data transmission and controlling MIMO transmission modes. It enables efficient spectrum access and sharing in time, frequency, and space, and adapts to varying network conditions. The evaluation results show that the proposed scheme greatly improves network performance compared to the traditional schemes.

EWU-TV: User-Centric Energy-Efficient Digital TV Broadcast Over Wi-Fi Networks

This paper presents an innovative multifaceted architecture, named energy-efficient framework for DTV broadcast over Wi-Fi networks (eWU-TV), that provides an energy efficient, user-centric, adaptive digital television (DTV) broadcast over Wi-Fi. To cater to the varied properties of the user equipments (UEs), the proposed framework broadcasts DTV content in the form of scalable video coded content that is adapted to suit the subscribers' requirements. The user-centricity is in terms of UE device display size, user preferences for video quality profile based on device energy saving, and UE transmission technology support (DVB-T/H or Wi-Fi). Mathematical models on device battery discharge, quality of experience, and user preference are devised that closely approximate the results of device battery discharge experiments on DTV reception by heterogeneous devices over Wi-Fi/DVB-T, subjective video quality assessment study, and statistical survey of user preference. The proposed eWU-TV performance optimization framework is based on the developed models. The framework ensures that the adaptive scalable broadcast reception via Wi-Fi serves more number of users with higher quality of user experience and with provisions for significant device energy saving.

An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies.

BackgroundA unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented.MethodsTo rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data.ResultsThe results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs.ConclusionThe approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.

Performance Analysis and Optimization of TDMA Network With Wireless Energy Transfer

With wireless energy transfer capability, network nodes can rely on the energy supplied wirelessly from a network hub or access point. As a result, there is no need for the nodes to replace or recharge their battery using any wire. This paper considers such a scenario and proposes the performance analysis and optimization framework for the network operating on a TDMA protocol with wireless energy transfer. We first present the analysis and optimization of an individual node in the network. The objective is to maximize the network utility defined in terms of throughput and the number of packets in the queue such that the packet loss probability is maintained below the threshold. We solve the optimization problem to obtain an optimal policy to operate the node (i.e, to be active or inactive). We next formulate the network optimization problem for the network hub. The problem can be solved to determine the amount of wireless energy transfer to meet the quality of service (QoS) requirements of all the nodes in the network. We reveal the special structure of the problem, that we can decompose the network optimization into small subproblems. These subproblems can be solved efficiently using the standard algorithm.

ON VECTORIZATION FOR LATTICE BASED SIMULATIONS

We present a vector-friendly blocked computing strategy for the lattice Boltzmann method (LBM). This strategy, along with a recently developed data structure, Structure of Arrays of Structures (SoAoS), is implemented for multi-relaxation type lattice Boltzmann (LB). The proposed methodology enables optimal memory bandwidth utilization in the advection step and high compute efficiency in the collision step of LB implementation. In a dense computing environment, current performance optimization framework for LBM is able to achieve high single-core efficiency.

Simultaneous investment, operations, and financial planning in supply chains: A value-based optimization approach

Asset utilization is a major mid-term lever to increase shareholder value creation. Since rough-cut planning of capacity (dis-)investments is performed at the long-term level, detailed timing of adjustments remains for the mid-term level. In combination with capacity control measures, capacity adjustment timing can be used to optimize asset utilization. This paper provides a corresponding framework for value-based performance and risk optimization in supply chains covering investment, operations, and financial planning simultaneously. We illustrate the benefits of the approach using a case-oriented example, and highlight the value of using flexible capacity options and postponing of capacity-related decisions in an uncertain environment.

Simultaneous Investment, Operations, and Financial Planning in Supply Chains: A Value-Based Optimization Approach

Asset utilization is a major mid-term lever to increase shareholder value creation. Since rough-cut planning of capacity (dis-)investments is performed at the long-term level, detailed timing of adjustments remains for the mid-term level. In combination with capacity control measures, capacity adjustment timing can be used to optimize asset utilization. This paper provides a corresponding framework for value-based performance and risk optimization in supply chains covering investment, operations, and financial planning simultaneously. We illustrate the benefits of the approach using a case-oriented example, and highlight the value of using flexible capacity options and postponing of capacity-related decisions in an uncertain environment.