Miracle-dz/UniFAR-FTUs huggingface dataset:
A Recommended System with a Solution Architect in Minimizing the Lead Time of Last Mile LPG Distribution in India

Reducing lead time to deliver orders placed by customers and organized operations in a system are among the influencing factors for any supply chain network to be effective. LPG cylinder distribution in India is one such business that requires organized operations and a quick delivery mechanism to deliver the order placed by the subscriber within a short period, thereby enhancing customer satisfaction. Mainly, companies like Indian Oil Corporation Limited (IOCL), Bharat Petroleum Corporation Limited (BPCL), and Hindustan Petroleum (HP) are significant stakeholders in LPG distribution in India and have set a target of delivery within a day post booking. Despite being an essential commodity, the distributors and the LPG companies find it challenging to deliver the cylinders within 24 hours post order placement due to various practical conditions. With this backdrop, this study proposes (a) a Standard Operating Procedure (SOP) and (b) conceptually a recommended system with a solution architect. The expected outcome of the study is to deliver the LPG cylinders within the committed lead time post order placement, thereby improving customer satisfaction.
Analysis of Hardware-Impaired Multi-cell Massive MIMO With Rician Fading And Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase-shift. The multi -cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BSs), which enables us to independently vary the resolution of ADC connected to each of its antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low-cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the LSFD vectors to maximize the sum SE metric using generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.
Using intuitive behavior models to rapidly adapt to and work with human teammates in Hanabi

An agent that can rapidly and accurately model its teammate is a powerful tool in the field of Collaborative AI. Furthermore, if an approximation for this goal was possible in the field of Human-AI Collaboration, teams of people and machines could be more efficient and effective immediately after starting to work together. Using the cooperative card game Hanabi as a testbed, we developed theChief agent, which models teammates using a pool of intuitive behavioral models. To achieve the goal of rapid learning, it uses Bayesian inference to quickly evaluate the different models relative to each other. To generate an accurate model, it uses historical data augmented by up-to-date knowledge and sampling methods to handle environmental noise and unknowns. We demonstrate that the Chief ’s mechanisms for modeling and understanding the teammate show promise, but the overall performance still can use improvement to reliably outperform a solution which skips inferring a best strategy and assumes all strategies in the pool are equally likely for the teammate.
PVT-Insensitive Ka-Band Class-D VCO with Average Current Control in 65 nm CMOS

In this paper, a PVT (process, voltage and temperature)-insensitive Ka-Band Class-D VCO with average current control in 65 nm CMOS process is presented. The proposed VCO is based on a Class-D VCO architecture to achieve good noise performance and low power consumption. Different from the traditional structure, a tail current source is added to control the average operation current of the VCO. This structure can achieve a stable VCO performance against PVT variations. And the output phase noise of the VCO is immune to the noise and ripples on the external power supply owing to the power supply rejection characteristic of the current source. Moreover, a typical internal power supply circuit is introduced and a minor improvement is addressed to realize the current source without deteriorating the total VCO phase noise. Quantitative analysis and simulation indicate that the proposed structure removes the strict requirements to the power supply circuits comparing to the traditional scheme. The experimental results show that at the frequency of 34.5 GHz, the phase noise of the proposed VCO is - 98 dBc/Hz at 1 MHz offset with the power supply circuit, the core power is 1.7 mW, and the core area is 0.09 mm2.
Sparsity Inducing System Representations for Policy Decompositions

Policy Decomposition (PoDec) is a framework that lessens the curse of dimensionality when deriving policies to optimal control problems. For a given system representation, i.e. the state variables and control inputs describing a system, PoDec generates strategies to decompose the joint optimization of policies for all control inputs. Thereby, policies for different inputs are derived in a decoupled or cascaded fashion and as functions of some subsets of the state variables, leading to reduction in computation. However, the choice of system representation is crucial as it dictates the suboptimality of the resulting policies. We present a heuristic method to find a representation more amenable to decomposition. Our approach is based on the observation that every decomposition enforces a sparsity pattern in the resulting policies at the cost of optimality and a representation that already leads to a sparse optimal policy is likely to produce decompositions with lower suboptimalities. As the optimal policy is not known we construct a system representation that sparsifies its LQR approximation. For a simplified biped, a 4 degree-of-freedom manipulator, and a quadcopter, we discover decompositions that offer 10% reduction in trajectory costs over those identified by vanilla PoDec. Moreover, the decomposition policies produce trajectories with substantially lower costs compared to policies obtained from state-of-the-art reinforcement learning algorithms.
Automatic Lane Change System with Double Loop PID Algorithm

For automatic lane change of the advanced driver assistance system, it is necessary to compensate the sensor limitations and also to provide a stable and accurate steering controller. In this paper, a look-ahead model that uses the future position data of the vehicle to supplement the limitations of the sensor mounted in the vehicle is used. In addition, to make smooth the lateral control of the vehicle body, a double loop PID is used to control the heading angle rate. Simulink-based program is used for the automatic lane change by manually operating the turn indicator in the vehicle. The program was simulated with IPG’s CarMaker and ran in a real-world environment with MicroAutoBox II on Rav4 of Toyota. Experimental results showed that both the real vehicle test and the simulation completed the lane change stably with an error within 50 cm, despite the influence of the disturbance in the real environment.
Calibration Regression of Censored Lifetime Medical Cost

Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates; however, the bivariate model error distribution is completely unspecified. Whereas this conceptually simple model is a natural multivariate generalization of the accelerated failure time model, this article proposes an inference procedure by extending the weighted log-rank estimating function to the marked point process framework. The resulting estimators are consistent and asymptotically normal. In addition, robustness and efficiency issues are discussed. Furthermore, a novel sample-based variance estimation procedure is proposed for estimators based on nonsmooth estimating functions in general. Finally, simulation studies show that this proposal is reliable for practical use. An illustration with application to a lung cancer clinical trial is provided.
Terrain Following of Tiltrotor UAV using MPC and Nonlinear Disturbance Observer-Based Backstepping

In this study, we designed a terrain following framework using model predictive control and nonlinear disturbance observer-based backstepping for a tiltrotor unmanned aerial vehicle (UAV). Terrain following with pitch hold is important in the operation of a tiltrotor UAV because of the uncooled infrared (IR) system and the avoidance of detection. Furthermore, a small tiltrotor UAV is more sensitive to wind disturbance near mountains. Therefore, we propose a nonlinear disturbance-based backstepping method for the flight path angle to enhance flight performance. Model predictive control is used to obtain the optimal flight path angle considering the constraints on the pitch and altitude. The numerical simulation results were compared with the model predictive control backstepping to verify the performance of the proposed framework.
Optimizing Synchronization Times for Position Tracking of a Mobile Asset in GPS-denied Environments

A network of distributed agents operating in a GPS-denied environment is tasked with tracking the position of a mobile asset. The agents use time-of-flight (ToF) measurements obtained from transmissions of a beacon signal by the asset to estimate the asset's position, but the estimates are noisy due to clock drift at the agents' local clocks. The clock drift can be reduced by having the asset and network of agents perform a distributed synchronization process; however, synchronization and localization cannot be performed simultaneously because the clock states are not well defined during synchronization and due to constraints on the radios' communication resources. The problem of optimizing when the agents should perform sensing or synchronization is formulated as a partially observed Markov decision process (POMDP) with continuous observations. Table-based Q-learning is used to search for an optimal policy on the belief space of the POMDP, but some form of approximation must be used because the belief space is a high -dimensional continuous space. We compare the performance of two approaches: 1) in replicated Q-learning, we learn a $Q$ function that is a linear function of the continuous beliefs; 2) in triple-Q learning, the beliefs are replaced by a model with fewer parameters, and quantization is used on a continuous parameter. Simulations are used to compare the performance of these methods with the approach that is most commonly used, which is using periodic synchronization at an optimized, fixed rate.
Redundant Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

A swarm of UAVs (S-UAVs) is a spontaneous wireless network composed of unmanned aerial vehicles (UAVs) cooperating together to upload data to the internet. In crisis cases such as flooding or earthquakes, one or more UAVs might be non-functional, resulting in a disconnected network. Due to the continuous changes in S-UAVs, one of the most reliable routing schemes is clustering. Clustering scheme splits the network into clusters with a cluster head and cluster members. The cluster head is responsible for all intra-cluster communication, which consists of communication between the clusters. The selection of the cluster head has a high impact on the performance of the routing scheme. This paper proposes a new clustered weighted scheme with redundancy to ensure end-to-end communication even with some non-functional UAVs. The proposed protocol has a new weighted formula for the primary cluster head selection. In addition, the proposed protocol selects the secondary cluster heads based on an elimination process. The parameters of the weighted formula are the distance, the speed, and the rewarding index. The rewarding index is calculated based on the latency. This protocol proposes the presence of a secondary cluster head that automatically replaces the primary cluster head whenever it is non-functional, thus ensuring the stability of the connection. The proposed scheme is simulated using MATLAB. The results obtained are discussed towards the end of this paper. The obtained results conclude that this is a promising protocol to decrease data loss in a crisis case scenario.
A Federated Learning Approach for Improving Security in Network Slicing

Network Slicing (NS) is a predominant technology in future telecommunication networks, including Fifth Generation (5G), which supports the realization of heterogeneous applications and services. It allows the allocation of a dedicated logical network slice of the physical network to each application. Security is one of the paramount challenges in an NS ecosystem. Several technologies, including Machine Learning (ML), have been proposed to mitigate security challenges in 5G networks. However, the use of ML for NS security is not properly implemented. Especially, the scarcity of coordination and the difficulties of privacy-protected information sharing between slices cause failures and performance degradation of these ML based NS security solutions. To address this issue, this paper proposes a novel Federated Learning (FL) based coordinated security orchestration architecture named Federated Learning enabled Security Orchestrator (FLeSO) to centrally perform security operations in a slicing ecosystem while preserving the privacy of the data. In addition, the proposed FLeSO architecture enables features such as proactive security deployment and steady security level maintenance independent of the slicing strategy. The proposed architecture is implemented in a real-world slicing testbed, and a comprehensive set of experiments are performed to evaluate the effectiveness of the proposed FLeSO architecture. The test results illustrate the significant advantage of the proposed approach over the legacy system in terms of improving the security of an NS ecosystem.
Energy Focusing for Wireless Power Transfer in the Near-Field Region

The future sixth generation (6G) wireless communications are envisioned to bring forth the era of the Internet of Everything (IoE). This work investigates wireless power transfer (WPT) in the radiating near-field region, as a medium for charging the low-powered IoE devices. Specifically, we exploit the near- field channel model in order to create power beamfocusing at a predefined focal point. We consider a uniform planar array employing beamfocusing, and provide analytical expressions for the harvested power at the receiver located at (i) a fixed and (ii) a random location in the network. We present numerical results which validate our analysis and draw an insight overview for the near-field WPT under various design parameters while demonstrating the gains brought against far-field WPT.
Passivity-Based Task Space Control of Hybrid Rigid-Soft (HyRiSo) Robots with Parametric Uncertainty

This paper introduces a novel robotic system, coined as a hybrid rigid-soft (HyRiSo) robot composed of rigid links and soft links serially attached. HyRiSo combines the benefits of the dexterity of a soft manipulator with the support capability of a classical stiff arm. Due to the heterogeneous modes of actuation for the revolute joints and to enable the bending of the soft links, it is challenging to design an integrated controller for this class of robots. We demonstrate in this paper that the well-known passivity-based adaptive and robust controllers can be utilized to address this challenge. Specifically, we use these controllers for task space tracking in the presence of uncertain mass, stiffness, damping and actuation. We demonstrate the effectiveness of the proposed HyRiSo robot by showing task space tracking in the presence of complex obstacles and joint limits. We provide numerical examples using a 2-rigid-2-soft HyRiSo robot and compare the performance of the proposed task space controllers illustrating the efficacy of these frameworks.
Overcome medical image data scarcity by data augmentation techniques: A review

Deep Learning brings back today valuable answers to many issues relating to different areas of life. To maximize the performance of a deep learning model, the model must be trained on a large amount of data. The scarcity of data is the first barrier to developing powerful medical image analysis-based deep learning systems. To overcome data scarcity, data augmentation techniques can be used to increase the diversity of the training dataset. This review summarizes most data augmentation techniques and exposes their performance before and after applying data augmentation. It also demonstrates the positive impact of using these techniques to overcome the lack of data in the medical imaging field.
Voltage Stability Enhancement Through Active Power Control of Converter-Interfaced Generation

The paper investigates the ability of Converter-Interfaced Generators (CIGs) to improve the stability margin of power systems by adjusting their active power injections at their points of connection. To this aim, a voltage magnitude-active power control scheme is examined and its effectiveness is tested considering different topology and CIG reactive power capability scenarios. The focus is on the effect on long-term voltage instabilities due to the system reaching its loadability limit, as well as on small-disturbance, short-term instabilities, arising in the form of Hopf bifurcations. Simulation results are based on a continuation power flow analysis carried out considering a modified version of the IEEE 39-bus system, where a portion of synchronous machines is replaced by CIGs.
The Class of -Cliqued Graphs: Eigen-Bi-Balanced Characteristic, Designs, and an Entomological Experiment

Much research has involved the consideration of graphs which have subgraphs of a particular kind, such as cliques. Known classes of graphs which are eigen-bi-balanced, that is, they have a pair a, b of nonzero distinct eigenvalues, whose sum and product are integral, have been investigated. In this paper we will define a new class of graphs, called q-cliqued graphs, on vertices, which contain cliques each of order connected to a central vertex, and then prove that these -cliqued graphs are eigen-bi-balanced with respect to a conjugate pair whose sum is and product . These graphs can be regarded as design graphs, and we use a specific example in an entomological experiment.
Byzantine Consensus Based on Modified Treap Topology

In the traditional Byzantine consensus schemes, the efficiency and throughput of the whole topology will be limited by transmission delay in nodes, which depends on the complexity of message and bandwidth constraints. This paper proposes a Byzantine consensus scheme based on weight modified Treap topology that reduces the complexity and delays of message aggregation by quickly constructing and maintaining Treap node tree. We have the master node organize other active nodes into a balanced tree rooted at itself to allocate communication and computing costs. We propose a failure detection mechanism for Treap that makes the master node record the weights of the nodes for each of its direct child nodes. The master node establishes a delay function to set the weight of each node based on the delay between receiving PREPARE message and completing its own calculation to sending its share. With a node's weight over threshold, it is assumed that the node has failed and been marked. The failure detection mechanism of the marked nodes starts to maintain availability by replacing the replication of the marked nodes. It also prevents the parent node from doing evil by authorizing nodes to mark only its direct child node. We only update the Treap topology on a regular basis or in case of failure to reduce the update overhead.
Lyapunov-based Transient Stability Analysis

The paper presents an analytical control solution to the problem of transient stabilization of lossy multi-machine power systems. Firstly, a new form of control Lyapunov function candidates with a flexible potential-energy-like term is proposed. This is achieved mainly by introducing an auxiliary state that contributes to the derivation of a cross-term. Based on the Lyapunov function candidates, a new control law ensuring asymptotic stability of the desired closed-loop operating equilibrium is proposed. Finally, a case study on the model of a two-machine system to illustrate the effectiveness of the proposed control solution is presented.
Peruvian Rural Jaggery Making Process: Heat Transfer Coefficients Determination in Pool Boiling Pans

This paper presents two approaches to calculate the overall heat transfer coefficients in different open heat exchangers used in the jaggery making process. These coefficients can be used in order to improve design methodologies of jaggery furnaces. The most important results are the calculation of the heat transfer coefficients: for forced convection and radiation from the flue gas to the heat exchanger, and for juice boiling inside the open heat exchanger. Temperature, flow velocities, gases concentration, and environment conditions were measured in order to develop the methodologies. These measurements were performed in a jaggery production module which was designed and built by researches of the Universidad de Piura, Peru. This production module has three types of heat exchangers. The influence of the geometry on temperatures, and the heat fluxes for convection and radiation are evaluated, as well as the different boiling heat transfer regimes.
Online Multi-Robot Task Assignment with Stochastic Blockages

In this paper we study the multi-robot task assignment problem with tasks that appear online and need to be serviced within a fixed time window in an uncertain environment. For example, when deployed in dynamic, human-centered environments, the team of robots may not have perfect information about the environment. Parts of the environment may temporarily become blocked and blockages may only be observed on location. While numerous variants of the Canadian Traveler Problem describe the path planning aspect of this problem, few work has been done on multi-robot task allocation (MRTA) under this type of uncertainty. In this paper, we introduce and theoretically analyze the problem of MRTA with recoverable online blockages. Based on a stochastic blockage model, we compute offline tours using the expected travel costs for the online routing problem. The cost of the offline tours is used in a greedy task assignment algorithm. In simulation experiments we highlight the performance benefits of the proposed method under various settings.