Two-level Framework Research Articles

Research on traffic zone partition method based on two-level partition theory

As an important part of traffic planning, traffic zone division has drawn much attention for decades. Better than traditional methods of traffic zone division based on traffic surveys and road network structures, this paper proposes a two-level framework for dividing traffic zones by using massive car-hailing data from a big data perspective. Firstly, the grid model is established within the study area and massive travel data are matched into grids. Secondly, the Louvain community detection algorithm is used to divide the research area into different traffic medium zones (TMZs). Further, a K-prototype clustering algorithm based on partition is used to divide each TMZ into several traffic small zones (TSZs). Taking the Beijing Fifth Ring as an example, the study area is divided into 16 TMZs and 302 TSZs by using two-level partition theory. The proposed method of dividing traffic zones is proved to be efficient and accurate and can be easily applied to other areas, which helps planners to make more smarter traffic planning and city management.

Transfer learning based 3D fuzzy multivariable control for an RTP system

Rapid thermal processing (RTP) is an important process in the fabrication of semiconductor devices. It is difficult to achieve temperature uniformity control of the wafer in RTP since the system is a highly nonlinear process with strong spatial distribution. In this study, a transfer learning-based three-dimensional (3D) fuzzy multivariable control scheme is proposed for the temperature uniformity control of an RTP system. In difference to the traditional expert-knowledge based design, a two-level framework of transfer learning methodology is constructed to design the 3D fuzzy multivariable controller (3D FMC) with the help of a multi-output support vector regression (M-SVR). The 3D FMC defines a qualitative spatial fuzzy structure that will be transferred to the M-SVR. On the other hand, the structure parameters of the M-SVR will be learned from data and transferred to design quantitative parameters of the 3D FMC. Under the framework of transfer learning, the control laws (e.g. human control experience) hidden in spatio-temporal data can be extracted and formulated back into multi-output 3D fuzzy rules. The proposed method provides an effective integration of the spatial fuzzy inference and the transfer learning for 3D FLC design. The newly developed method is applied to the temperature uniformity control of a rapid thermal chemical vapor deposition (RTCVD) system at the set temperature 1000K, and the maximum non-uniformity along the wafer radius is close to 1K.

Dynamic behavior of multi-carrier energy market in view of investment incentives

In this study, a dynamic two-level framework is proposed to model investment incentives in a multi-carrier energy market from a strategic company’s point of view. Capacity payment and firm contract are assumed as investment incentives to encourage the strategic producer to invest in generation units. In addition, financial incentives to invest in combined heat and power (CHP) include tax rebate and loans. Strategic company’s behavior is considered as a two-level model so that, in the first level, the objective function is to maximize the profit of the strategic producer by participating in an energy hub market. The strategic producer can invest in transmission lines, generation units, CHP, and gas furnace. In the second level, the aim is to maximize a multi-carrier energy social welfare encompassing heat, gas, and electric energy. In this model, units invested by rival companies are modeled using possible scenarios. Electric energy loads in this energy hub system are envisaged to be elastic, while heat loads are assumed to be inelastic to the market price. On the other hand, gas loads are indirectly elastic to the price. Besides, in the proposed framework, DC power flow and an exact gas flow model with the linearized Weymouth equation are used. The proposed model is implemented on two case studies including 6-bus system, and an energy hub system encompassing 24-bus IEEE RTS power system and 10-node natural gas system.

A command governor approach to set-theoretic model reference adaptive control for enforcing partially adjustable performance guarantees

In feedback control, the presence of system uncertainties cause system state trajectories to deviate from their ideal responses. In practice, a subset of these trajectories can be more critical than the rest due to physical and/or performance characteristics associated with a problem of interest. Hence, it is desired not only to have performance guarantees on the entire system state trajectories but also to be able to adjust the resulting worst-case performance bound for that critical subset. Yet, in model reference adaptive control of uncertain dynamical systems, assigning performance bounds on a subset of system trajectories is not trivial. This paper addresses this gap by proposing a new control architecture that has the capability to enforce a user-defined performance bound on the selected subset of dynamical system trajectories, entitled as partially adjustable performance guarantees. The proposed architecture is predicated on a set-theoretic treatment and utilizes a two-level constructive design framework. In particular, we first form an auxiliary state dynamics in order to construct the auxiliary system error vector between uncertain dynamical system states and this auxiliary dynamics states. This construction aids a control designer to weigh each element of the auxiliary system error vector independently, while enforcing performance bounds on the norm of this error vector. Then, a command governor mechanism is designed for driving a feasible user-selected subset of system states to a close (and user-controllable) neighborhood of the corresponding reference model states. This results in adjustable performance guarantees on a subset of system error trajectories.

Two-level game analysis of Japan in the TPP negotiations

Starting his second term as Japanese prime minister in late 2012, Shinzo Abe regarded Japan’s participation in TPP negotiations as one of the top priority foreign policy issues. Abe successfully acquired a concession from President Barack Obama in order to enter the TPP negotiations without prior commitment to the total elimination of tariffs. After entry into the negotiations, Japan found a way to offer a tariff elimination on the politically sensitive agricultural products. Although the Japanese negotiation team was a latecomer in the TPP negotiation, its members played an instrumental role in reaching an agreement by helping other negotiating partners in the process. In addition, other factors including Japan-Australia FTA, the break-up of the Japan-US cabinet level meeting, and the Chinese initiative in AIIB motivated the American negotiators to reach an agreement. Japan’s role became even more important with the US withdrawal from the agreement, eventually contributing to a successful conclusion of the TPP agreement among 11 countries. Employing a two-level game framework, this study examines how Japan reached an important agreement with the United States in the original TPP negotiations, and then, after the American withdrawal, with other countries in the TPP 11 negotiations.

Too big to fail? The dynamics of EU influence and fiscal consolidation in Italy and Spain (2008–2016)

ABSTRACTThe article studies the dynamics of fiscal consolidation and public sector reforms in Italy and Spain under the EU governance that took shape as a reaction to the Eurozone crisis. We show how three types of EU pressure – fiscal and economic coordination rules, conditionality, and back-room diplomacy have operated in conjunction. We also show that Italy was more willing than Spain to resist EU pressure. Based on a Two-Level Game framework, we argue that this can be explained by the greater opposition to European integration that has developed in Italy compared to Spain.

Online Learning for Energy Saving and Interference Coordination in HetNets

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user traffic periods has been proved to be an effective energy saving strategy. However, this strategy has strong interactions with interference coordination (IC) mechanisms, making it convenient to address both tasks simultaneously. The motivation of this paper is to develop a self-optimization algorithm capable of jointly controlling energy saving and IC mechanisms using an online learning approach. Our proposal is based on a contextual bandit formulation that, among other challenges, implies discovering the most energy-efficient control actions while satisfying a predefined level of Quality of Service (QoS) for the users. We propose a two-level framework comprising a global controller, in charge of a group of macro cells, and multiple local controllers, one per macro cell. The global controller implements a novel algorithm, referred to as the Bayesian Response Estimation and Threshold Search (BRETS), that is capable of learning, for each control action, its feasibility boundaries in terms of QoS and its energy consumption as a function of the aggregated user traffic. The algorithm comes with a bound on its expected convergence time. The local controllers translate the control actions learned by the global controller into local decisions. Our numerical results show that BRETS is only 1% less efficient than an ideal oracle policy, clearly outperforming other benchmark algorithms.

The GDPR and the research exemption: considerations on the necessary safeguards for research biobanks

The General Data Protection Regulation (GDPR) came into force in May 2018. The aspiration of providing for a high level of protection to individuals’ personal data risked placing considerable constraints on scientific research, which was contrary to various research traditions across the EU. Therefore, along with the set of carefully outlined data subjects’ rights, the GDPR provides for a two-level framework to enable derogations from these rights when scientific research is concerned. First, by directly invoking provisions of the GDPR on a condition that safeguards that must include ‘technical and organisational measures’ are in place and second, through the Member State law. Although these derogations are allowed in the name of scientific research, they can simultaneously be challenging in light of the ethical requirements and well-established standards in biobanking that have been set forth in various research-related soft legal tools, international treaties and other legal instruments. In this article, we review such soft legal tools, international treaties and other legal instruments that regulate the use of health research data. We report on the results of this review, and analyse the rights contained within the GDPR and Article 89 of the GDPR vis-à-vis these instruments. These instruments were also reviewed to provide guidance on possible safeguards that should be followed when implementing any derogations. To conclude, we will offer some commentary on limits of the derogations under the GDPR and appropriate safeguards to ensure compliance with standard ethical requirements.

Parameter Combination Framework for the Differential Evolution Algorithm

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.

Two-level modeling of lithium-ion batteries

The widely used Pseudo-Two-Dimensional (P2D) cell model for Li-ion batteries is generally based on a simplified lithium diffusion model of active particles with certain geometry. In this work, we propose a two-level framework, which extends the P2D cell model and incorporates a mechanically coupled phase-field Cahn–Hilliard diffusion model of active particles. The phase-field model allows more detailed diffusion study in particles of general geometry and includes the full coupling of the mechanics and phase separation. To improve the computational efficiency, we manage to reduce one degree of freedom at the cell level by treating the ion flux between the electrolyte and active particle as a dependent quantity. The two-level framework is validated against the original one and applied to study the impact of particle geometry, finite deformation elasticity and phase separation on the cell performance. Results show that the oblate particle has better cell performance than other spheroidal particles. It is attributed to the mechanical drifting at the higher curvature.

Bayesian framework for direction of arrival analysis using spherical harmonics

A common task in acoustical applications is the determination of directions of arrival (DoAs) of sound at a receiver. This work aims to address this problem in situations involving potentially multiple simultaneous sound sources by means of a two-level framework of Bayesian inference. This process involves first estimating the number of sound sources present, followed by estimating their directional information, based on sound data collected with a spherical microphone array. Analytical models are formulated using spherical harmonic beamforming techniques, which are incorporated into the Bayesian analysis as part of the prior information. The experimental data are also incorporated to update the information available prior to analysis. All necessary prior information is assigned based on the principle of maximum entropy. Through this technique, the number of sources is first estimated, and then, the DoA information of those sources is extracted from the most concise model that adequately fits the experimental data. This paper presents the Bayesian formulation and analysis results to demonstrate the potential usefulness of model-based Bayesian inference for determining DoAs in complex noise environments with potentially multiple concurrent sources.

Optimization of Network-Load Interaction With Multi-Time Period Flexible Random Fuzzy Uncertain Demand Response

This paper represents a network-load interaction optimization framework integrated multi-time period flexible random fuzzy uncertain demand response (DR) model, which involved shifting of loads’ operation time and uncertain response to the price signal. The network-load interaction is to optimize load profile and network topology under the guidance of electricity price at the aim of achieving the economic and secure operation of the distribution network and at the same time satisfying the consumers’ satisfaction. This framework is a two-level framework, which includes the price-based DR level to minimize the daily load variance and maximize the customers’ satisfaction, and the reconfiguration level to minimize the network reconfiguration cost and power unbalancing. Firstly, the price-based DR level determines the price and the load profile subject to the multi-time period flexibility and uncertainties of the DR. Then, the reconfiguration level optimizes the network configuration topology according to the load profile and feeds the results back to the price-based DR level. This network-load interaction optimization model is tackled by the proposed multi-objective self-adaptive particle swarm (SAPSO) optimization algorithm. The proposed network-load interaction optimization model is applied to the IEEE33-bus distribution system and a real system. The results show that this model is efficient to solve the network economic operation and load profile optimization problem simultaneously.

A Network Analysis-Based Distributed Load Shedding Strategy for Voltage Collapse Prevention

This paper proposes a network analysis-based distributed load shedding strategy for voltage collapse prevention. The two-level framework of the distributed load shedding strategy is proposed. The lower one is consisted of local load shedding controllers, and the upper one is designed to coordinate the local controllers to reduce the total load shedding amount. Through network analysis, two sensitivity indices are derived to form local zones, and determine the load shedding locations and amounts. The voltage control sensitivity is proposed for network partitioning. The local load shedding controller is solved by load shedding sensitivity based piecewise linearization. A load shedding sensitivity based linear programming problem is formulated for the center load shedding controller to coordinate the load shedding amounts of local controllers. Case studies have been conducted to verify the effectiveness of the proposed methods.

Optimal Operation of Battery Energy Storage System Considering Distribution System Uncertainty

We present a novel control strategy of charging and discharging batteries in a distribution system to optimize the energy transaction cost. With an increased proportion of renewable energy in a distribution system, the real demand curve may significantly deviate from the forecast curve, which can lead to an increased challenge for an energy distribution company in making an effective purchase plan. The proposed strategy aims at tracking the total forecast demand curve, and can mitigate risk and encourage demand-side bidding. In this paper, short-term load forecasting, wind power forecasting, and solar power forecasting are performed. To optimize profit, the optimal operation of energy storage systems in a distribution system was developed and solved in a two-level framework considering forecast uncertainties in day-ahead operation and mitigating the net demand gap in real-time operation. To quantify the risk mitigation and profits, the purchase strategies for uncertain and certain demand that occurs on the next day were compared. The promising results show that optimal operation of a battery energy storage system can reduce the energy cost and the transaction risk for an energy distribution company.

Experience as a source of knowledge in divestiture decisions: emerging issues and knowledge management implications

PurposeThis paper aims to analyse the idea that experience acts as an antecedent in divestiture and triggers an organisational learning process that enables the divesting firm to convert experience into knowledge, increasing the probability that a firm will undertake subsequent divestitures.Design/methodology/approachThe approach is quantitative. The research project used a case–control design, with a sample consisting of 274 divesting and non-divesting firms. Given the dichotomous nature of the dependent variable, the relations of the research model are tested using logistic regression.FindingsThe likelihood of a divestiture increases when firms have already had past experience of divestitures. Firm performance and firm size act as moderating variables, that is, the learning effects are weaker in firms with better past performance and also in larger firms.Research limitations/implicationsThe study contributes to the literature on organisational learning and divestiture. In particular, the knowledge obtained from previous divestitures is positively related to subsequent ones. The results on firm size and performance as contingency factors make it possible to distinguish between the different learning mechanisms in proactive and reactive divestitures, as well as in larger and smaller firms. Accordingly, a two-level framework of experience and knowledge is proposed.Practical implicationsThe results are of interest for practitioners who need a better understanding of the antecedents of their strategic actions in terms of past experience and knowledge. The study also offers insights into the knowledge management practices that fit into the proposed two-level framework of knowledge accumulation.Originality/valueThe originality of the study consists in the strong evidence of learning effects in divestitures that it finds. This study augments a promising line of research on the effect of experience in rare strategic decisions, enriching our understanding of the learning mechanisms associated with complex experiences.

Protein fold recognition using Deep Kernelized Extreme Learning Machine and linear discriminant analysis

Protein fold recognition is considered as an essential step in determining the tertiary structure of proteins in bioinformatics. The most complex challenge in the protein folding problem is the high dimensionality of feature vectors and the diversity of the protein fold classes. In this paper, two frameworks are proposed to solve this problem. The main components in the first two-level framework are Deep Kernelized Extreme Learning Machine (DKELM) and linear discriminant analysis. Second framework consists of three levels. In the first level, the dataset is initialized to be used in the next level. In the second level, OVADKELM and OVODKELM are independently employed to extract four and six new features, respectively, which are added into the basic datasets in the third level. DKELM is applied in the third level as a final classifier to classify the instances into folds. The proposed frameworks are implemented on DD and TG datasets which are considered as SCOP datasets. The experimental results indicate that proposed methods could improve the classification accuracy in both datasets.

Two-Level Spectral Methods for Nonlinear Elliptic Equations with Multiple Solutions

The present paper provides a two-level framework based on spectral methods and homotopy continuation for solving second-order nonlinear boundary value problems exhibiting multiple solutions. Our pr...

Evolving roles and structures of triadic engagement in healthcare

PurposeThe purpose of this paper is to focus on the changing nature of healthcare service encounters by studying the phenomenon of triadic engagement incorporating interactions between patients, local and virtual networks and healthcare professionals.Design/methodology/approachAn 18-month longitudinal ethnographic study documents interactions in naturally occurring healthcare consultations. Professionals (n=13) and patients (n=24) within primary and secondary care units were recruited. Analysis of observations, field notes and interviews provides an integrated picture of triadic engagement.FindingsTriadic engagement is conceptualised against a two-level framework. First, the structure of triadic consultations is identified in terms of the human voice, virtual voice and networked voice. These are related to: companions’ contributions to discussions and the virtual network impact. Second, evolving roles are mapped to three phases of transformation: enhancement; empowerment; emancipation. Triadic engagement varied across conditions.Research limitations/implicationsThese changing roles and structures evidence an increasing emphasis on the responsible consumer and patients/companions to utilise information/support in making health-related decisions. The nature and role of third voices requires clear delineation.Practical implicationsStructures of consultations should be rethought around the diversity of patient/companion behaviours and expectations as patients undertake self-service activities. Implications for policy and practice are: the parallel set of local/virtual informational and service activities; a network orientation to healthcare; tailoring of support resources/guides for professionals and third parties to inform support practices.Originality/valueContributions are made to understanding triadic engagement and forwarding the agenda on patient-centred care. Longitudinal illumination of consultations is offered through an exceptional level of access to observe consultations.

An Adaptive Privacy Policy Prediction for User Uploading Images and Shared Data

An Adaptive Privacy Policy Prediction (A3P) system to support users to comprise privacy settings for their images. With the accumulative volume of images, user’s stake through social sites, sustaining privacy has become a major problem, as proven by a recent trend of publicized happenings where users unintentionally shared personal information. In such a case of incidents, the need of tools to help users control access to their shared content is superficial. Towards addressing this need, it is examined the role of social context, image content, and metadata as probable indicators of users’ privacy partialities. A two-level framework which is rendering to the user’s available history on the site, defines the best available privacy policy for the user’s images being uploaded. The solution depend on an image classification framework for image categories which may be accompanied with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the created policies will follow the evolution of users’ privacy attitude. It also provides the results of extensive evaluation which determine the efficacy of the system, with prediction accuracies.

Optimization of postbuckling behaviour of variable thickness composite panels with variable angle tows: Towards “Buckle-Free” design concept

Variable angle tow (VAT) laminates that generally exhibit variable stiffness properties not only provide extended design freedom, but also offer beneficial stress distributions. In this paper, the prospect of VAT composite panels with significantly reduced loss of in-plane compressive stiffness in the postbuckled state in comparison with conventional structures, is studied. Specifically, we identify that both thickness and local fiber angle variation are required to effectively define “Buckle-Free” panels under compression loading. In this work, the postbuckling behaviour of variable thickness VAT composite panels is analysed using an efficient and robust semi-analytical approach. Most previous works on the postbuckling of VAT panels assume constant thickness. The additional benefits of tailoring thickness variation in the design of VAT composite panels are seldom studied. However, in the process of manufacturing VAT laminates, either by using the conventional Advanced fiber Placement (AFP) machine (tow overlap) or the newly developed Continuous Tow Shearing (CTS) process (tow shrink) thickness build-up is inevitable. The postbuckling optimization for the design of VAT layups is conducted by a two-level framework using lamination parameters as intermediate design variables. The objective is to determine optimal lamination parameters and thickness distributions for maximizing the axial compressive stiffness of VAT laminates that are loaded in the postbuckling regime. The thickness variation due to both manufacturing of VAT laminates and for where it is independent of manufacturing process are considered. In accordance with the first-level optimal postbuckling solutions in terms of lamination parameters, we investigate a practical “Buckle-Free” VAT panel using a blended layup configuration. This blended VAT panel consists of a piecewise combination of segmental CTS layers and constant-thickness VAT layers. The prospect of taking advantage of a benign combination of stiffness and thickness to improve the overall compressive strength of VAT panels is studied. Finally, the optimal results are analysed to provide insight into the manufacturing of VAT laminates using either the AFP or the CTS process for improved postbuckling stiffness under compression loading.