Two-layer Approach Research Articles

A model-free approach for auto-tuning of model predictive control

A two-layer approach for the auto-tuning of model predictive control (MPC) is proposed. The bottom layer computes the weighting matrices of the cost function from a desired closed-loop bandwidth while the top layer aims at finding the optimal bandwidth. This optimum corresponds to the optimal balance between the robustness and nominal performance of the closed-loop system. To find the optimal bandwidth, the extremum seeking (ES) algorithm, a form of non-model-based adaptive optimisation, is proposed. The auto-tuning approach is tested on a binary distillation column model. It is shown that the auto-tuning approach enables the MPC system to track its optimal closed-loop bandwidth and therefore obtain the minimum output variance.

A Preliminary Glance at Social Innovation from an Ontological Point of View

After presenting four basic ontological frameworks for social being, the paper adopts the two-layered approach defended by Bhaskar and Poli. Within this framework, the relation between emergents and latents is briefly described. Since most emergents are ephemeral (weak signals), the problem arises of what may eventually stabilize emergents, and values are seen as promising stabilizers for emerging new behaviors. By exploiting the case of technological innovation, the paper raises the broader issue of social innovation and the problem of its stabilization.

A Heuristic-Based Planner and Improved Controller for a Two-Layered Approach for the Game of Billiards

In the past , we have proposed a two-layered approach to compute a winning strategy for the game of Billiards. AI tools as well as robust optimization routines for noisy environments were combined to plan the sequence of shots. We complete the modeling here by introducing significant developments for the high-level planner which guides the precise optimal controller to generate a plan given at any random initial state. We will first resume the general model for this particular class of problems and then propose several domain-specific heuristics to guide our search and render the problem tractable. Several improvements to the optimal robust controller, including refinements in the objective function, will also be presented in order to improve single-shot optimization. Results are presented demonstrating the full potential of the methods proposed making it the state of the art in regards to the game of Billiards.

Force feedback control design for nonideal teleoperators

Achieving force feedback for a nonideal teleoperator is challenging, due to complications such as friction, force sensor noise, non-backdriveability and structural resonances. Furthermore, non-collocation of the force sensors and the point of interaction results in shunt dynamics that degrade the interaction force estimation. In this paper, a method is presented to model, identify and compensate for the influence of shunt dynamics. Furthermore, a recently developed two-layer approach that enforces passivity in the time domain is implemented and evaluated in a practical setup that is dedicated for application in surgery. Experiments demonstrate that using a combination of these techniques with an impedance reflecting controller, stable bilateral interaction with both soft and hard environments is achieved, for a nonideal system. A teleoperated robot for minimally invasive surgery is used as a representative example of a nonideal surgical system.

New Conceptual Model for Soil Treatment Units: Formation of Multiple Hydraulic Zones during Unsaturated Wastewater Infiltration

Onsite wastewater treatment systems are commonly used in the United States to reclaim domestic wastewater. A distinct biomat forms at the infiltrative surface, causing resistance to flow and decreasing soil moisture below the biomat. To simulate these conditions, previous modeling studies have used a two-layer approach: a thin biomat layer (1-5 cm thick) and the native soil layer below the biomat. However, the effect of wastewater application extends below the biomat layer. We used numerical modeling supported by experimental data to justify a new conceptual model that includes an intermediate zone (IZ) below the biomat. The conceptual model was set up using Hydrus 2D and calibrated against soil moisture and water flux measurements. The estimated hydraulic conductivity value for the IZ was between biomat and the native soil. The IZ has important implications for wastewater treatment. When the IZ was not considered, a loading rate of 5 cm d resulted in an 8.5-cm ponding. With the IZ, the same loading rate resulted in a 9.5-cm ponding. Without the IZ, up to 3.1 cm d of wastewater could be applied without ponding; with the IZ, only up to 2.8 cm d could be applied without ponding. The IZ also plays a significant role in soil moisture distribution. Without the IZ, near-saturation conditions were observed only within the biomat, whereas near-saturation conditions extended below the biomat with the IZ. Accurate prediction of ponding is important to prevent surfacing of wastewater. The degree of water and air saturation influences pollutant treatment efficiency through residence time, volatility, and biochemical reactions.

A Topic Description Model Based on Two-Layer Kl Distance

The main challenge of Topic Detection and Tracking (TDT) for Blog is the insufficient information in a topic description and the lack of key words input by users. We propose a Two-layer KL Distance approach which combines the KL distance model with a lexical semantic association matrix model. First, the KL Distance model captured the weights of Initial feature words. Second, the KL Distance model was used again to estimate weights of words linked with initial feature words in the lexical Semantic Association Matrix. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of the two-layer of KL Distance.

Fate and transport modelling of urban highway contaminants by a multi-objective evolutionary method

A hybrid mechanistic data-driven approach was used to identify optimal structures for the processes of a mechanistic build-up wash-off model for predicting the continuous pollutographs of various constituents from urban highway surfaces. The mechanistic model is based on mass balance and the advective–dispersive transport of pollutants in runoff. Using the pollutograph data of seven constituents (TSS, DOC, Cr, Cu, Ni, Pb and Zn) collected from highly urbanized highway sites in California, we applied the two-layer model identification approach to find unique optimum functional forms that represent the processes and their optimum parameter values. The comparison of the model results and observed data indicate acceptable agreement for the examined constituents and rain events. The build-up and wash-off model developed using this approach honours the physical processes involved and is a reliable tool for predicting constituent pollutographs as well as understanding the physical and underlying processes.

Active tracking and pursuit under different levels of occlusion: a two-layer approach

We present an algorithm for real-time, robust, vision-based active tracking and pursuit. The algorithm was designed to overcome problems arising from active vision-based pursuit, such as target occlusion. Our method employs two layers to deal with occlusions of different lengths. The first layer is for short- or medium-term occlusions: those where a known method--such as mean shift combined with a Kalman filter--fails. For this layer we designed the hybrid filter for active pursuit (HAP). HAP utilizes a Kalman filter modified to respond to two different modes of action: one in which the target is positively identified and one in which the target identification is uncertain. For long-term occlusions we use the second layer. This layer is a decision algorithm that follows a learning procedure and is based on game theory-related reinforcement (Cesa-Bianchi and Lugosi, Prediction Learning and Games, 2006). The learning process is based on trial and error and is designed to perform adequately with a small number of samples. The algorithm produces a data structure that can be shared among agents or sent to a central control of a multi-agent system. The learning process is designed so that agents perform tasks according to their skills: an efficient agent will pursue targets while an inefficient agent will search for entering targets. These capacities make this system well suited for embedding in a multi-agent control system.

Ensemble of online neural networks for non-stationary and imbalanced data streams

Concept drift (non-stationarity) and class imbalance are two important challenges for supervised classifiers. “Concept drift” (or non-stationarity) refers to changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Research on classification of non-stationary and imbalanced data streams, mainly focuses on batch solutions, whereas online methods are more appropriate. Here, we propose an online ensemble of neural network (NN) classifiers. Ensemble models are the most frequent methods used for classifying non-stationary and imbalanced data streams. The main contribution is a two-layer approach for handling class imbalance and non-stationarity. In the first layer, cost-sensitive learning is embedded into the training phase of the NNs, and in the second layer a new method for weighting classifiers of the ensemble is proposed. The proposed method is evaluated on 3 synthetic and 8 real-world datasets. The results show statistically significant improvement compared to online ensemble methods with similar features.

New Insights on the Molecular Recognition of Imidacloprid with Aplysia californica AChBP: A Computational Study

The binding of imidacloprid (IMI), the forerunner of neonicotinoid insecticides, with the acetylcholine binding protein (AChBP) from Aplysia californica, the established model for the extracellular domain of insects nicotinic acetylcholine receptors, has been studied with a two-layer ONIOM partition approach (M06-2X/6-311G(d):PM6). Our calculations allow delineating the contributions of the key residues of AChBP for IMI binding. In particular, the importance of Trp147 and Cys190-191, through weak CH···π interactions and both van der Waals and hydrogen-bond (H-bond) interactions, respectively, are highlighted. Furthermore, H-bonds between hydroxyl groups of both Ser189 and Tyr55 and the IMI nitro group are pointed out. The participation of Ile118, whose main chain NH and carbonyl group are hydrogen-bonded with the IMI pyridinic nitrogen through a water molecule, is characterized. Our simulations also indicate the presence of a significant contribution of this residue through van der Waals interactions. The various trends obtained by the calculations of the pairwise interaction energies are confirmed through a complementary noncovalent interaction (NCI) analysis of selected IMI-AChBP amino acid pairs. Indeed, the contribution of a halogen-bond interaction between IMI and AChBP, recently proposed in the literature, is corroborated by our NCI analysis.

Domination of Constrained Application Protocol: A Requirement Approach for Optimization of Internet of Things in Wireless Sensor Networks

The protocols related to the Internet of Things (IoT) have proven to undergo changes in the Wireless Sensor Network (WSN), to increase the compatibility of WSN with the industrial and automation applications. In this paper, the programming model, the framework and the security aspects for the Constrained Application Protocol (CoAP) are analyzed with respect to the WSN. CoAP chooses the two-layered approach, to deal with interchanging of messages. The IOT involves implementing the programming in the Representational State Transfer (REST) model paradigm, which is predominantly inherited by the CoAP protocol. The CoAP proxy adapts a backend framework in a modular form. Also, the Datagram Transport Layer Security (DTLS) overcomes unreliable nature in the CoAP due to the User Datagram Protocol (UDP). The CoAP protocol is studied from different angle with respect to the prominent aspects for implementation of IOT in a real world.

Domination of Constrained Application Protocol: A Requirement Approach for Optimization of Internet of Things in Wireless Sensor Networks

The protocols related to the Internet of Things (IoT) have proven to undergo changes in the Wireless Sensor Network (WSN), to increase the compatibility of WSN with the industrial and automation applications. In this paper, the programming model, the framework and the security aspects for the Constrained Application Protocol (CoAP) are analyzed with respect to the WSN. CoAP chooses the two-layered approach, to deal with interchanging of messages. The IOT involves implementing the programming in the Representational State Transfer (REST) model paradigm, which is predominantly inherited by the CoAP protocol. The CoAP proxy adapts a backend framework in a modular form. Also, the Datagram Transport Layer Security (DTLS) overcomes unreliable nature in the CoAP due to the User Datagram Protocol (UDP). The CoAP protocol is studied from different angle with respect to the prominent aspects for implementation of IOT in a real world.

Optimal Time-varying Operation of Nonlinear Process Systems with Economic Model Predictive Control

In the this work, we propose a two-layer approach to dynamic economic optimization and process control for optimal time-varying operation of nonlinear process systems. The upper layer, utilizing a Lyapunov-based economic model predictive control (LEMPC) system, is used to compute dynamic economic optimization policies for process operation. The lower layer, utilizing a Lyapunov-based MPC (LMPC) system, is used to ensure that the closed-loop system state follows the optimal time-varying trajectories computed by the upper layer over each finite-time operating window. To improve the computational efficiency of the two-layer structure, we allow both the LEMPC and the LMPC to compute control actions for two distinct sets of manipulated inputs thus decreasing the real-time computational demand compared to other one-layer EMPC schemes. Following a rigorous formulation and analysis of the proposed method, we demonstrate boundedness of the closed-loop system state and closed-loop economic performance improvement w...

Gaussian-based multiconfiguration time-dependent Hartree: A two-layer approach. I. Theory

We describe a novel two-layer variant of the Gaussian-based multiconfiguration time-dependent Hartree (G-MCTDH) approach which improves on the performance and convergence properties of quantum propagation based on variationally evolving frozen Gaussians (FGs). While the standard scheme uses factorizable multi-dimensional FGs, the present approach combines these into flexible, MCTDH-like single-particle functions. At the same time, the expensive variational evolution of the Gaussian parameters is reduced to low-dimensional subspaces. As a result, the novel scheme significantly alleviates the current bottleneck to accurate propagation in G-MCTDH and its variational multiconfigurational Gaussian (vMCG) variant. Since the first-layer single-particle functions are chosen to be orthogonal, the present approach can be straightforwardly combined with existing multi-layer MCTDH schemes.

Air entrainment in transient flows in closed water pipes : A two-layer approach

In this paper, we first construct a model for free surface flows that takes into account the air entrainment by a system of four partial differential equations. We derive it by taking averaged values of gas and fluid velocities on the cross surface flow in the Euler equations (incompressible for the fluid and compressible for the gas). The obtained system is conditionally hyperbolic. Then, we propose a mathematical kinetic interpretation of this system to finally construct a two-layer kinetic scheme in which a special treatment for the "missing" boundary condition is performed. Several numerical tests on closed water pipes are performed and the impact of the loss of hyperbolicity is discussed and illustrated. Finally, we make a numerical study of the order of the kinetic method in the case where the system is mainly non hyperbolic. This provides a useful stability result when the spatial mesh size goes to zero.

Ovarian biopsy: a non-terminal method to determine reproductive status in giant kokopu, Galaxias argenteus (Gmelin 1789)

AIM: To establish a method of gonad biopsy for ovarian tissue collection in the declining giant kokopu Galaxias argenteus (Gmelin 1789) as an alternative to lethal sampling in order to understand the species' reproductive biology. METHODS: Six female giant kokopu weighing between 200 and 350 g were caught from the wild in early December of 2009 and transferred to a holding facility (Department of Zoology, University of Otago, Dunedin) where they were kept under a simulated natural photo-thermal regime for 10 months. Fish were repeatedly biopsied for ovarian tissue at near-monthly intervals (mean number of days between biopsies = 33) until ovulation. RESULTS AND CONCLUSIONS: Ovarian samples were successfully collected from giant kokopu by biopsy for use in downstream analyses. Among a total of 23 biopsy events, a single death occurred when a two-layered suturing approach was used, highlighting the value of this method for study of the reproductive biology of valuable fish. CLINICAL RELEVANCE: This biopsy method may have implications for veterinary research on fish physiology, pathology, conservation and development, when repeated tissue samples need to be collected over a prolonged period of time or for general surgical manipulations on fish when accessing the coelom. Furthermore, this approach allows the implementation of a more powerful experimental design, as repeated measures reduces the variability of estimates due to the removal of inherent stage differences among individuals.

Assisted navigation for a brain-actuated intelligent wheelchair

This paper presents an Assistive Navigation System (ANS) for a Robotic Wheelchair (RW) relying on a Brain–Computer Interface (BCI), as the Human–Machine Interface (HMI). A two-layer collaborative control approach is proposed to steer the RW, taking into account both user and machine commands. The first layer, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on context. More specifically, user commands are enabled for a set of situations requiring user decision, namely, bifurcations, multiple-directions caused by new obstacles in the environment, and deadlocks. The second layer is a user-intent matching responsible for determining the suitable steering command that better fits the user selection, taking into account the user competence to steer the wheelchair, and situation awareness of potential directions at a given location. A P300-based BCI allows the selection of commands to steer the RW. Experimental results using RobChair (Pires and Nunes (2002) [7], Lopes et al. (2007) [42]) are presented, showing the effectiveness of the proposed methodologies. The ANS was validated with ten able-bodied participants, and one participant with cerebral palsy, in two different scenarios: a structured known environment, and a structured unknown environment with moving objects. The overall result was that all participants were able to successfully operate the device, showing a high level of robustness of both, the BCI system, and the navigation system.

Oblivious Sequential Decode and Forward Cooperative Strategies for the Wireless Relay Channel

In a dynamic wireless network where a source terminal communicates with a destination, in presence of other users in the network, it is worthwhile to consider oblivious relaying strategies of a relay in close proximity to the source transmitter. The source-relay channel is assumed to be a fixed gain additive white Gaussian noise (AWGN) channel due to source-relay colocation. The source-destination and the relay-destination transmissions are subject to block flat fading dynamics. A perfect channel state information (CSI) at the respective receivers only is assumed. With the expected throughput as a performance measure, we incorporate a two-layer broadcast approach into a cooperative strategy based on the decode-and-forward scheme - Sequential Decode-and Forward (SDF). The broadcast approach splits the transmitted rate into superimposed layers, corresponding to a "bad" and a "good" channel states, allowing better adaptation to the actual channel conditions. The achievable rate expressions for the SDF strategy are derived under the broadcast approach for multiple settings including single user, multiple-input single-output (MISO) and the general relay setting using successive decoding technique, both numerically and analytically. Continuous broadcasting lower bounds are derived for the MISO and for the oblivious cooperation scenarios.

Management of Large Bowel Injury During Laparoscopic Surgery

Management of Large Bowel Injury During Laparoscopic Surgery

Two-Layer Wrapping for COTS Software Integration: An Experience with Matlab

Although commercial-off-the-shelf (COTS) product integration presents clear advantages in a variety of engineering fields, several problems can arise in part due to their heterogeneous nature. Little research addresses the integration of particular COTS to specific domains. A University of Vigo project for reusable software development in the engineering domain adopted this approach. The underlying hypothesis was that making COTS integration easier would facilitate the development of domain-specific applications. The solution consists of a two-layer wrapping approach. The first layer captures the COTS domain model, facilitating integration of general-purpose functionality. A second layer provides better integration of more domain-specific functionality. Several real software development projects have used the proposed solution, and the results yielded notable effort savings, showing the approach's utility in reducing COTS integration efforts.