Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Tuning Network Control Architectures

We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design.
Flexible Information Propagation in Oscillator Networks

Proper functioning of a variety of complex systems relies on flexible communication between different units. Yet, how context-dependent information propagation routes arise over a relatively static network remains largely mysterious. Inspired by the observation that neuronal communication is regulated by oscillations in the brain, we propose a dynamical information transmission framework based on the Kuramoto-oscillator network. We analytically show that multistability is a mechanism for flexible information routing. We define a novel metric, the flexibility index, to measure the communication plasticity of information channels in networks. Sufficient conditions on the network parameters are obtained for channels to be flexible. We also use several examples to illustrate how information can be routed to different parts of a network via switching between multiple stable states.
Computation Offloading for Latency Reduction in Regionalized Hierarchical Vehicular Fog Network

Regionalized communication and computation architecture aggregates idle vehicular fog computation resources in urban cities without dedicated infrastructure deployment. Vehicular fog computing migrates computation services close to the edge of the network, which can break the long response latency limitation of cloud computing. However, the high mobility and random distribution of vehicles lead to the difficulty of the fog infrastructure deployment and the offloading decision. In this paper, we investigate the latency minimization problem in the regionalized three-tier vehicular fog network, which is divided into the sub-problems of single-region offloading decision and multi-region resource allocation. In particular, we make the preliminary offloading decision for users in each roadside unit (RSU) coverage area to solve the single-region offloading decision problem. We also transfer the multi-region resource allocation problem into a group-knapsack problem to obtain the optimal resource allocation results among all the users in the base station coverage area. Simulation results validate the superiority of our proposed algorithm, which enables latency reduction in the regionalized three-tier vehicular fog network.
Coordinated Control of Air-conditioning Load and Battery Energy Storage System for Improving Electricity Supply-demand Balancing

In the day-ahead electricity market, the next day's demand is scheduled one day before. Deviations from the forecasted demand on the day of operation results in costly penalties for the retailer. This study proposes a method for coordinating the operation of a Heating Ventilation and Air-conditioning (HVAC) load and Battery Energy Storage System (BESS) to reduce the occurrence of the imbalance. The proposed method changes the HVAC operation within the comfortability range of indoor temperature when demand forecast error is low and uses the changed power to charge/discharge BESS. When a significant forecast error occurs, the charged/discharged energy in BESS in a prior period is also used in addition to HVAC load adjustment to suppress the imbalance further. In a sample simulation, the average load imbalance of 6.6% was eliminated with the proposed method, whereas it was only reduced to 5.4% with HVAC curtailment alone.
The Effective Zone Routing Protocol Design Using Deep Recurrent Neural Network for The Next Generation Wireless Network

This study proposes the usage of LSTM-RNN to allow ZRP to adjust the value of zone radius to the environment by sizing each node’s routing zone based on network performance input metrics such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Those input metrics were used as a dataset, and split into 500 as data training, and 100 as data testing to get the zone radius value as an output value in the simulation. The proposed algorithm was tested in two scenarios: a static node environment and a mobility node environment using MATLAB as a simulator. The bandwidth capacity used in this study is 300 Mbps, which meets the requirement of next-generation wireless networks (5G and beyond). Furthermore, the proposed algorithm’s (LSTM-RNN ZRP) results are compared to conventional ZRP in both scenarios. The range of zone radius for mobile node environments is wider than for static node environments, with a range of 2-6 for LSTM-RNN ZRP and 2-7 for conventional ZRP. Meanwhile, the range for mobile node environments is 1-7 for both LSTM-RNN ZRP and conventional ZRP. According to the relationship between input metrics and zone radius determination, the proposed algorithm is more effective when used in a static node environment. However, both algorithms are acceptable for application in a static and mobile node environment.
Attention Cooperative Task Offloading and Service Caching in Edge Computing

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the computing delay of many emerging applications. Nevertheless, The limited storage capacity of edge servers requires judicious design of service caching as well as task offloading to maximize edge computing performances. In this paper, we formulate a cooperative task offloading, service caching, and transmit power allocation problem to minimize the cost of computing delay and energy consumption of UEs. To address this problem, we propose a graph attention based multi-agent deep deterministic policy gradient (GAT-MADDPG) algorithm, in which a multi-headed graph attention mechanism is incorporated into the centralized critic network to learn the attentive cooperation policies. Simulation results show that the proposed GAT-MADDPG algorithm exhibits an effective performance improvement.
Path Following Control for Human-Robot Collaborative Tasks

This paper presents a path following control designed for human-robot collaborative tasks. The path following strategy allows the task description and human-robot interaction to be defined according to components tangent and transversal to a nominal path. With this formulation, the control objectives for each task can be customized to generate motions towards task completion and getting on the nominal path when switching between tasks. Simulation examples of the path following control is presented, demonstrating the ability for the proposed controllers to be customized according to objectives of the human-robot collaboration.
Pseudo-Gradient Dynamics with Cognitive Predictions in Noncooperative Dynamical Systems

A framework of pseudo-gradient-based noncooperative systems with Level-k thinking under bounded depth of reasoning is proposed. In the characterized framework, each agent believes that he/she is the most sophisticated person in the noncooperative system and is allowed to base their decisions on the predictions about the likely actions of other agents. Depending on a knowledge network of payoff functions, the agents may be able to reasoning the other agents’ best-response states and use these predicted states in the pseudogradient dynamics. Some sufficient conditions are presented to guarantee stability of a Nash equilibrium with uncertain sensitivity parameters or uncertain knowledge network of payoff functions. We present a numerical example to illustrate the efficacy of our approach.
Design and Analysis of Millimeter Wave Tetra-Band Antenna Operating in Ka/V/W Band for Military and Radar Applications

This study presents a promising tetra band Mm-wave antenna $\boldsymbol{(9\times 8\times 0.127 \ \text{mm}^{3})}$ with a full ground plane for Ka, V and W-band applications. A rectangular 2-shaped resonator microstrip line antenna including the optimized shape is implemented. The tapered and truncated edges have been introduced to optimize the radiation pattern and overall performance. At the centered frequencies of 34 GHz, 62 GHz, 76 GHz and 93.7 GHz the proposed multiband antenna exhibits noticeable gain of 7.48 dB, 6.81 dB, 8.88 dB and 10.90 dB respectively. At the antenna's 76 GHz frequency band, a maximum bandwidth of 1.4 GHz is achieved. Thus, the suggested tetra-band antenna is the perfect candidate for high speed military and radar applications.
Application of the BIM Methodology in Hospital Constructions

In Chile, a high number of hospital establishments are in the project, design and construction stages, and due to its complexity, the use of the BIM methodology constitutes an opportunity to take advantage of all its potential for the benefit of the timely and good results of these works, especially considering its social impact. This paper examines the definitions and functionalities of BIM within hospital projects and presents some cases of hospital projects that have used the methodology to obtain the benefits it offers. And the contribution that biomedical engineering can make to the comprehensive development of projects. It concludes with different aspects to consider for the successful and multidisciplinary implementation of the BIM methodology in hospital projects.
Research on ICN Caching and Pricing Strategies under the Package Billing Model

Information-Centric Networking (ICN) is a commercially viable network architecture that enables content and location separation through in-network caching, reducing dupli-cate traffic and improving network resource utilisation. As with other networks, a reasonable pricing mechanism can facilitate the deployment of ICNs by encouraging operators to participate in the deployment of ICNs. A large number of studies on ICN pricing mechanisms have been conducted in which users pay for traffic on a per-unit basis, as opposed to the real-life method of paying for traffic on packages set by operators. In this paper, based on studying the interaction between users and ISPs and CPs and establishing the utility functions of each role, we analyse the caching and pricing strategies of each entity under NASH equilibrium and compare and analyse which charging model is more able to meet the needs of network entities in the ICN environment. It is found that the package billing model is more in line with the needs of operators and users in I CN s, and can achieve the objective of incentivising ICN development. This paper also contributes to the development of the best pricing strategy for ICN networks.
Bayesian Inference of Total Least-Squares With Known Precision

This paper provides a Bayesian analysis of the total least-squares problem with independent Gaussian noise of known variance. It introduces a derivation of the likelihood density function, conjugate prior probability-density function, and the posterior probability-density function. All in the shape of the Bingham distribution, introducing an unrecognized connection between orthogonal least-squares methods and directional analysis. The resulting Bayesian inference expands on available methods with statistical results. A recursive statistical identification algorithm of errors-in-variables models is laid- out. An application of the introduced inference is presented using a simulation example, emulating part of the identification process of linear permanent magnet synchronous motor drive parameters. The paper represents a crucial step towards enabling Bayesian statistical methods for problems with errors in variables.
EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.
Convergence Rates of Decentralized Gradient Dynamics over Cluster Networks: Multiple-Time-Scale Lyapunov Approach

We present a new analysis for the performance of decentralized consensus-based gradient (DCG) dynamics for solving optimization problems over a cluster network of nodes. This type of network is composed of a number of densely connected clusters with sparse connections between them. Decentralized consensus algorithms over cluster networks have been observed to constitute two-time-scale dynamics, where information within any cluster is mixed much faster than the one across clusters. In this paper, we present a novel analysis to study the convergence rates of the DCG methods over cluster networks. In particular, we show that these methods converge at a rate ln(T)/T and only scale with the number of clusters, which is relatively small to the size of the network. Our result improves the existing analysis when applied to study the rates of DCG over cluster networks, where these rates are shown to scale with the size of the network. The key technique in our analysis is to consider a novel Lyapunov function that captures the multiple time-scale dynamics of DCG in cluster networks. We also illustrate our theoretical results by a number of numerical simulations using DCG dynamics over different cluster networks.
Event-Triggered Cloud-based Nonlinear Model Predictive Control with Neighboring Extremal Adaptations

Model predictive control (MPC) is a popular optimal control approach that can explicitly deal with system constraints. However, its high computational cost motivates researches on various approximation methods that make the MPC more practically viable for systems with fast dynamics and/or limited on-board computing power. Neighboring extremal (NE) is one such method that can be used to adapt to a nominal open-loop MPC sequence based on state perturbations so that it alleviates the online computational burden and extends the applicability of the MPC. In this paper, a cloud-based nonlinear MPC (NMPC) framework is considered where the NMPC is first performed on the cloud by taking advantage of its powerful computation and storage capabilities. To save the computational power, instead of solving the NMPC problem at every time step, the open-loop control sequence is downloaded for on-board implementation, then the NE is employed to systematically update the control sequence based on the deviation of the measured/estimated state from the nominal state obtained by the cloud-based NMPC. However, due to unknown disturbances and request-response communication delays between the plant and the cloud, the cloud-based NMPC may not show a reasonable performance for the plant even with the NE adaptations when the deviation is large. Therefore, we develop an event-triggered scheme based on a cost criteria to strike a balance between performance and cost. Finally, the efficacy of the proposed control synthesis is demonstrated with simulations on the application of the cart inverted pendulum.
Strategies for measuring risk in public use microdata files

Statistical agencies have the responsibility to design data release strategies which will not violate pledges of nondisclosure either through intent or neglect. In addition to ethical and legal concerns, statistical offices must be mindful that violating pledges of confidentiality may undermine an agency's ability to collect data due to loss of public trust. Statistical organizations also have the obligation to make information available to a variety of individuals and institutions to allow for informed discussion from differing perspectives on a range of issues. However, it is through fine, accurate detail on a file that risks of disclosure arise. In this report we discuss strategies for controlling risk in the release of public use microdata files. The equivalence class structure of a microdata file is defined and we show how the classic entropy function can be employed on the equivalence class structure to provide a measure of relative risk.
Multi-Application Battery Energy Storage Systems for Energy Arbitrage and Frequency Regulation: Singapore Context Analysis

Battery energy storage systems (BESSs) alleviate the renewable energy generation intermittency and can also realize revenue through participation in multiple applications, including energy arbitrage and ancillary services. This paper investigates cost effectiveness of behind-the-meter BESSs to reduce system costs and provide frequency regulation service. The BESS is modeled to support different operational modes, including energy arbitrage and frequency regulation service, either one at a time or concurrently. To evaluate the cost effectiveness of the BESS in various operational modes, we conduct simulations based on existing rules of Singapore's electricity market for behind-the-meter BESSs. Our simulation results show that BESSs' multi-application, in general, can reduce the system cost. In Singapore context, the resulting cost reduction becomes limited due to the existing metering rules.
Discovering Truth in Mobile Crowdsensing with Differential Location Privacy

The combination of Mobile Crowdsensing (MCS) and truth discovery has benefited the ubiquitous monitoring and analysis of the physical world. To address the concerns alongside user data collection, the literature has partially studied data privacy protection for truth discovery. Yet, the threats of location leakage remain overlooked in such contexts. For joint accommodating privacy protection and truth elaboration, we propose to leverage differential privacy for distributed user location obfuscation and explore spatial correlation for corresponding observation's value calibration. We form this process into a truth estimation deviation minimization problem under differential privacy and obfuscation requirements. By theoretically transforming it into probabilistic calibration residual optimization, the problem can be solved via linear programming. Evaluation on real-world temperature and humid sensing data shows its effectiveness on providing significant location distortion distance and practically acceptable time consumption. Results also reveal an up to 53% truth discovery accuracy improvement compared to the SCP baseline.
3-D Analytical Model of Axial-Flux Permanent Magnet Machine With Segmented Multipole-Halbach Array

This paper presents a 3-D analytical model of an axial-flux permanent magnet (AFPM) machine with a segmented multipole-Halbach PM array. Closed-form solutions are self-consistently derived in terms of modified Bessel functions of the first- and the second-kind by solving analytically Laplace and Poisson equations by the method of magnetic scalar potential subject to the appropriate boundary conditions. In the preceding studies, their formulations are based on a 2-D or quasi 3-D geometry, and their discussions are often limited to the magnetic fields with low-poles of the regular PM. The proposed model successfully provides more rigorous and widely applicable expressions for magnetic fields, back-electromotive force, Lorentz torque and torque constant without limitations on the number of poles and the arrangements of the PM. Behavior of the torque constant is then shown against the number of poles ranging widely from low-poles to high-poles of the regular PM, the standard-Halbach PM and the multipole-Halbach PM for changeable geometrical parameters. The obtained results are of much use in understanding intrinsically the performance characteristics of the AFPM.
Instability Assessment of AlGaN/GaN High Electron Mobility Transistors Under High Drain Current Condition

Detailed analysis of the reliability instability of the AlGaN/GaN High-Electron-Mobility-Transistors was presented in this work. Two AlGaN/GaN HEMTs with similar structures, the difference being the channel thickness and channel back barrier were compared for the assessment. The high electric field stress measurements were performed for the long-term reliability test. The threshold voltage degradation (ΔVT) was observed to be compatible with the fast transient trapping of the electrons at the AlGaN barrier layer, which was restored partially by providing a relaxation voltage. Excluding the fast-transient charging components, the power-law exponent (n) for time-dependent threshold voltage deterioration in the tch = 40 nm channel device (n = 0.15) is considerably smaller than in the tch = 420 nm channel device (n = 0.26). The shorter time exponent explains that the interface degradation component has less of an impact on the tch = 40 nm device than the tch = 420 nm device. Higher ΔVTshift is most likely connected to the transconductance (Gm) deterioration. Furthermore, the tch = 40 nm device showed higher and fmax of 120 GHz and 230 GHz compared to the tch = 420 nm device's and fmax of 55 GHz and 117 GHz.
Research on Vehicle Parking Position Detection Based on Swin Transformer Semantic Segmentation

Determine whether the vehicle is parked in the specified area, which has high application value in industry, transportation, parking lot, etc. Aiming at the problems of rough results and high maintenance costs, a vehicle parking position detection method based on Swing Transformer semantic segmentation is proposed. The vehicle semantic results obtained by Swin Transformer semantic segmentation algorithm are taken as the main features of vehicles in the picture. Canny algorithm is used to obtain vehicle contour to improve detection accuracy. Calculate the relationship between the vehicle contour and the hand drawn warning line, and compare with the threshold value to determine whether the vehicle is parked in the specified area. Through simulation, industrial application and road application, the method can realize the normative detection of vehicle parking position.