Performance Tuning Research Articles

Introduction

Introduction

A Community Ambience

This paper analyzes sound-oriented public performance Tuning In—the neighborhood, a project by Tao G. Vrhovec Sambolec, which took place in Amsterdam in May 2019. Through a combination of micropolitical theory and consideration of the (sound) art heritage that Tuning In is part of, the term ambience is transformed into a concept that helps one make sense of the way sound and place combine to effect one’s comprehension of the space they are in. Weaving in and out of the event in question while drawing on descriptions of the contemporary, sound studies, the philosophy of community, and affect theory, this article concludes by recognizing Tuning In as a sonic apparition of a communal spirit. It argues that this was an event in which one could find an amplification of an altermodernity rooted in the commons and the anti-neoliberal. Through a historically rooted understanding of the community that made itself heard during this event, Tuning In is presented as a vibrational amplification of the sort of community that is at threat in a gentrifying city. While also highlighting the potential problems arising from community making, such as the exclusion of certain bodies, this paper argues that a community must have the space within which it can “tune in” to itself and produce its own ambience.

Exploiting stance hierarchies for cost-sensitive stance detection of Web documents

Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through a 4-class classification model where the class distribution is highly imbalanced. Therefore, they are particularly ineffective in detecting the minority classes (for instance, 'disagree'), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes, which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important 'disagree' class.

Design of a fuzzy PID controller for a MEMS tunable capacitor for noise reduction in a voltage reference source

This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise.Article HighlightsThis article deals with MEMS tunable capacitor dynamics and modeling, considering measurement noise.It designs and applies fuzzy PID control system for regulating MEMS voltage reference output.This paper contributes to robustness increase in pull-in performance of the tunable capacitor.

Performance tuning of glass fiber/epoxy composites through interfacial modification upon integrating with dendrimer functionalized graphene oxide

AbstractIn this study, glass fiber/epoxy composites were interfacially tailored by introducing polyamidoamine (PAM) dendrimer functionalized graphene oxide (GO) into epoxy matrix. Two different composites each containing varying loading fraction (0.5, 1.0, and 1.5 wt%) of GO and GO‐PAM were fabricated via hot press processing. Composites were evaluated for interlaminar shear strength (ILSS), dynamic mechanical properties and thermal conductivity. The inclusion of 1.5 wt% GO‐PAM resulted ~57.3%, ~42.7%, and ~54% enhancement in ILSS, storage modulus and thermal conductivity, respectively. Almost, ~71% reduction in coefficient of thermal expansion was also observed at same GO‐PAM loading. Moreover, higher glass transition temperature was observed with GO‐PAM addition. GO‐PAM substantially improved fiber/matrix interfacial adhesion, which was witnessed through scanning electron microscopy. The enhanced thermo‐mechanical performance was attributed to interfacial covalent interactions engendered by ring opening reaction between epoxy and amine moieties of PAM dendrimers. These multiscale composites with extraordinary functional properties can outperform conventional counterparts with improved reliability and performance.

Progress in transcription factor-based metabolite biosensors

Transcription factor-based biosensors (TFBs) play an essential role in metabolic engineering and synthetic biology. TFBs sense the metabolite concentration signals and convert them into specific signal output. They hold high sensitivity, strong specificity, brief analysis speed, and are widely used in response to target metabolites. Here we reviewe the principles of TFBs, the application examples, and challenges faced in recent years in microbial cells, including detecting target metabolite concentrations, high-throughput screening, adaptive laboratory evolutionary selection, and dynamic control. Simultaneously, to overcome the challenges in the application, we also focus on reviewing the performance tuning strategies of TFBs, mainly including traditional and computer-aided tuning strategies. We also discuss the opportunities and challenges that TFBs may face in practical applications, and propose the future research trend.

Tuning the hydraulic permeability and molecular weight cutoff (MWCO) of surface nano-structured ultrafiltration membranes

Hydraulic permeability and molecular weight cutoff (MWCO) performance tuning of polysulfone (PSf) ultrafiltration (UF) membrane was attained via surface modification with a tethered layer of poly(acrylic acid) (PAA). Surface nano-structured (SNS) PAA layer was synthesized onto base PSf UF membranes by atmospheric pressure plasma (APP) surface activation followed by graft polymerization (GP) of acrylic acid (AA). Through a systematic study, it was shown that effective SNS-PAA-PSf UF membrane performance tuning was feasible by adjustments of APP and graft polymerization conditions. It was shown, for the first time, that SNS-PAA-PSf membranes can be synthesized with a range of hydraulic permeability (spanning a factor of 1.1–2.6 in magnitude) for a given MWCO, or a range of MWCO (spanning a factor of 1.5–2.3 in magnitude) for a given hydraulic permeability, thereby overcoming the hydraulic permeability-MWCO tradeoff.

Prospects for Electrical Performance Tuning in Ca3Co4O9 Materials by Metallic Fe and Ni Particles Additions.

This work further explores the possibilities for designing the high-temperature electrical performance of the thermoelectric Ca3Co4O9 phase, by a composite approach involving separate metallic iron and nickel particles additions, and by employing two different sintering schemes, capable to promote the controlled interactions between the components, encouraged by our recent promising results obtained for similar cobalt additions. Iron and nickel were chosen because of their similarities with cobalt. The maximum power factor value of around 200 μWm−1K−2 at 925 K was achieved for the composite with the nominal nickel content of 3% vol., processed via the two-step sintering cycle, which provides the highest densification from this work. The effectiveness of the proposed approach was shown to be strongly dependent on the processing conditions and added amounts of metallic particles. Although the conventional one-step approach results in Fe- and Ni-containing composites with the major content of the thermoelectric Ca3Co4O9 phase, their electrical performance was found to be significantly lower than for the Co-containing analogue, due to the presence of less-conducting phases and excessive porosity. In contrast, the relatively high performance of the composite with a nominal nickel content of 3% vol. processed via a two-step approach is related to the specific microstructural features from this sample, including minimal porosity and the presence of the Ca2Co2O5 phase, which partially compensate the complete decomposition of the Ca3Co4O9 matrix. The obtained results demonstrate different pathways to tailor the phase composition of Ca3Co4O9-based materials, with a corresponding impact on the thermoelectric performance, and highlight the necessity of more controllable approaches for the phase composition tuning, including lower amounts and different morphologies of the dispersed metallic phases.

Behind the Breakthrough of the ∼30% Perovskite Solar Cell

In the December 11, 2020 issue of Science, Al-Ashouri and colleagues reported a certified monolithic perovskite/silicon tandem solar cell with the power conversion efficiency 29.15%. The improvements of the ideality factor and the fill factor are key to achieving efficient perovskite tandem solar cells, which lead a new direction of the perovskite research.

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for “hot” tasks to be migrated to run on a “cool” core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this article, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50 percent reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.

Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems

This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm.

A Survey on Data-driven Performance Tuning for Big Data Analytics Platforms

Many research works deal with big data platforms looking forward to data science and analytics. These are complex and usually distributed environments, composed of several systems and tools. As expected, there is a need for a closer look at performance issues. In this work, we review performance tuning strategies in the big data environment. We focus on data-driven tuning techniques, discussing the use of database inspired approaches. Concerning big data and NoSQL stores, performance tuning issues are quite different from the so-called conventional systems. Many existing solutions are mostly ad-hoc activities that do not fit for multiple situations. But there are some categories of data-driven solutions that can be taken as guidelines and incorporated into general-purpose auto-tuning modules for big data systems. We examine typical performance tuning actions, discussing available solutions to support some of the tuning process's primary activities. We also discuss recent implementations of data-driven performance tuning solutions for big data platforms. We propose an initial classification based on the domain state-of-the-art and present selected tuning actions for large-scale data processing systems. Finally, we organized existing works towards self-tuning big data systems based on this classification and presented general and system-specific tuning recommendations. We found that most of the literature pieces evaluate the use of tuning actions at the physical design perspective, and there is a lack of self-tuning machine-learning-based solutions for big data systems.

Influence of Al-doped ZnO transparent electrodes on thin-film interference in Cu2ZnSn(S,Se)4 thin-film solar cells prepared via a sputtering method

Transparent electrodes are often regarded as a trade-off between transmittance and sheet resistance, although the effect of thin-film interference on the performance of an optoelectronic device is significant. In this paper, we report the effect of optical thin-film interference of an Al-doped ZnO (AZO) transparent electrode on the performance of Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. First, we distinguished the transmittance in the overall wavelength region (450–800 nm) of the AZO transparent electrode from the transmittance in the maximum absorption region for CZTSSe solar cells (550–700 nm). From the results, a thicker AZO transparent electrode showed higher transmittance (87.1%) than that of a thinner AZO transparent electrode (82.9%) in the maximum absorption region. These electrodes were applied in CZTSSe solar cells, which showed a difference of up to 26% in power conversion efficiency. In addition, the colors generated by the optical interference between the AZO transparent electrodes. We anticipate that our results will be useful for improving the performance and/or color tuning of thin-film solar cells for application in building-integrated photovoltaics.

Effect of heteroatom doping and morphology tuning of CNT-derived material for potassium-ion hybrid capacitors

Carbon-based materials are all-around anodes for potassium-ion (K+) storage systems. To further boost the electrochemical performance, heteroatom doping (“HD”) and morphology tuning (“MT”) are two prominent ways. However, the detailed influence of “HD” and “MT” is still confused with each other, leading to a derived question that does the preparation sequence between “HD” and “MT” affect the ultimate performance? To reveal these questions, multi-wall carbon nanotube (CNT) is selected as a precursor, and further modulated to N/S dual-doped carbon with hierarchical morphology and corresponding counterparts, respectively. By comprehensive comparisons, “MT” shows an advantage in introducing structural imperfection, making full use of surface absorption and capacitive contribution, while “HD” is good at expanding interlayer, facilitating the diffusion/intercalation of K+. The revealed individual effects of “HD” and “MT” give a convenience to elucidate the role of preparation sequence played in the ultimate performance, subsequently guiding to synthesize N/S dual-doped carbon with hierarchical morphology derived from CNT. Such subtle structure design balances the capacity and capacitive contribution, and shows excellent performance in K-ion hybrid capacitor (a high power density of 21428.6 W kg−1 and remarkable long cycle life of 14,000 times). This unique construction of superior universal anode with great performance provides a new avenue for next storage development and commercial application.

Modeling and analyzing the performance of high-speed packet I/O

Recently, 10 Gbps or higher speed links are being widely deployed in data centers. Novel high-speed packet I/O frameworks have emerged to keep pace with such high-speed links. These frameworks mainly use techniques, such as memory preallocation, busy polling, zero copy, and batch processing, to replace costly operations (e.g., interrupts, packet copy, and system call) in native OS kernel stack. For high-speed packet I/O frameworks, costs per packet, saturation throughput, and latency are performance metrics that are of utmost concern, and various factors have an effect on these metrics. To acquire a comprehensive understanding of high-speed packet I/O, we propose an analytical model to formulate its packet forwarding (receiving--processing--sending) flow. Our model takes the four main techniques adopted by the frameworks into consideration, and the concerned performance metrics are derived from it. The validity and correctness of our model are verified by real system experiments. Moreover, we explore how each factor impacts the three metrics through a model analysis and then provide several useful insights and suggestions for performance tuning.

Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

Databases and warehouses are experiencing workload of different types such as Decision Support System (DSS), Online Transaction Processing (OLTP) and Mixed workloads. Handling variety of workload in autonomic systems is a critical task. After self-configuring the workload, the next challenge is workload performance tuning that motives towards self-predictive systems. Existing studies provide performance modeling solutions on small-scale data repositories of Database Management System (DBMS) and Data Warehouse (DWH) using either classical eager or lazy learning approaches. However, in real-world problems, we normally have to deal with large-scale data repositories. Therefore, there is a need to develop performance models that provide data augmentation to solve large-scale data repositories that are not publicly available. In this study, deep learning approaches have been investigated for performance tuning of large-scale data repositories. We propose a performance prediction model called Optimized GAN-based Deep Learning (OGDL) model. For data augmentation, Conditional Generative Adversarial Networks (CGAN) is applied. For autonomic perspective, we incorporated MAPE-K model to manage the workload autonomically. Different deep learning models are applied, and it was observed that Deep Belief Network (DBN) performed better as compared to other deep learning models such as Deep Neural Network (DNN). We performed a number of experiments and from results it is observed that deep learning models performed the best in comparison with classical machine learning and lazy learning and a $6-8\%$ increase in accuracy is recorded in our experiments using DBN. The proposed OGDL model performed the best in workload performance predictions in an optimized way.

On the Role of Hash-Based Signatures in Quantum-Safe Internet of Things: Current Solutions and Future Directions

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized “things” with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as the elliptic curve cryptosystem (ECC). Nevertheless, the reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this article, we investigate applications of one of the postquantum signatures called hash-based signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with an emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for IoT security in the quantum era. We also investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.

Finite-Time Tracking Controller Design of Perturbed Robotic Manipulator Based on Adaptive Second-Order Sliding Mode Control Method

This article presents an adaptive finite time second-order sliding mode tracking control scheme for robotic manipulators. A new control law is suggested to force the trajectories of the robot manipulator to move from all initial conditions to proportional-integral-derivative switching surface in the finite time and stay on it. Moreover, the adaptation law rejects the requirement of knowledge about upper bound of the external perturbations. It is difficult to exactly determine the upper bound of the perturbations in the practical systems, such as robot manipulators. Unlike the existing methods, the new adaptive finite time second-order sliding mode tracker for $n$ -link robot manipulators enables accurate tracking control, robust performance and parameter tuning. The suggested approach presents the design of a robust controller such that the tracking errors of robot manipulator can reach the equilibrium in the finite time. Through the combination of the finite time tracker and disturbance observer, the position tracking purpose of manipulator joints is accurately performed not only in the nominal environment, but also in the existence of different types of perturbations. The robustness performance and effectiveness of the offered technique are studied in simulation and experimental results.

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of e-learning features to optimize the e-learning models with available Machine learning techniques from peer-inspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of e-learning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.

基于飞秒激光的Pt-TiO2纳米连接及其电学性能调控

基于飞秒激光的Pt-TiO2纳米连接及其电学性能调控