Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kind of Optimal Investment Problem under Inflation and Uncertain Exit Time

This paper is concerned with a kind of optimal portfolio choice problem, in which an investor can invest his wealth either in a bank account or in an real project with production. The bank pays a lower interest rate for deposit and takes a higher rate for any loan. We take inflation and uncertain exit time into consideration and transform it into a control problem. For deterministic market parameters case, we employ dynamic programming principle to obtain the optimal strategy. For stochastic case, we get a explicit solution by the solution of two stochastic Riccati equations (SREs) for constant relative risk aversion (CRRA) case. Moreover, we present several numerical examples to show the application of theoretical results and further discuss the influence of inflation and uncertain exit time.
State Estimation for Discrete Time-Delay Complex Networks with Switching Topologies and Binary Encoding Schemes

In this paper, the state estimation is investigated for a class of discrete-time delayed complex network with switching topologies and stochastic noises subject to binary encoding scheme (BES). The Markov chain is utilized to describe the random changes of network topologies. By using the BES, the original measurement signal is first encoded into a bit string, then transmitted via binary symmetric channels, and lastly restored by a decoder before entering the estimator. In the transmission process, random bit errors may occur in the bit string due to the channel noises. A set of Bernoulli distributed random variables is used to characterize such errors. The aim of this paper is to design a mode-dependent state estimator such that the estimation error dynamics is exponentially ultimately bounded in the mean square. A sufficient condition of the expected estimator is given by utilizing Lyapunov functional approach, and subsequently the estimator gain matrix is obtained by solving a certain matrix inequality. Finally, a simulation example is shown to certify the effectiveness of the state estimation scheme.
The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme

AbstractThe Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting.
Detection of Cyber-Attacks in Smart Power Transmission System using Mathematical Morphology and Autoencoder

Cyber-security of the transmission protective relays is essential for the safety and reliable operation of the smart grid. The increased adoption of the IoT-based smart relays and lack of encryption in the IEC 61850 protocol-based substation automation system has raised the risk of false data injection attacks on the protection relays. The attackers of the power system can easily trip the potential transmission lines by tampering with the measurement data that is vital for the protective relay operation. This work proposes a mathematical morphology and deep learning autoencoder-based technique to detect such attacks on overcurrent relays. The mathematical morphology extracts the most significant features of the measured data, which are then supplied as an input to the autoencoder, distinguishing the attacked measurements from the actual fault measurements. The autoencoder distinguishes the maliciously injected faulty data from the actual fault data by calculating the reconstruction error. To validate the proposed technique, a two-bus benchmark system is used. The result shows that the proposed method is capable of attack detection with excellent precision and satisfactory recall rates for the overcurrent protection scheme. Also, the effect of the severity of different attacks on the performance of the proposed technique is analyzed and is being found that the performance of the proposed technique remains satisfactory even during low severity conditions.
High-Gain Dual-Mode Cylindrical Rectangular Patch Antenna for Airborne Applications

This article presents a conformal antenna system for providing a dual-band high-gain communication link between an unmanned aerial vehicle (UAV) and a ground station where the two bands can be used independently for command-control and data link. A cylindrically conformal rectangular patch antenna operating in the higher order TM21 and TM03 modes is proposed, which produces high gain in both the frequency bands. This is possible by loading the cylindrical–rectangular patch antenna by a centrally located slot, which makes the radiation pattern of the TM21 mode broadside radiating and lowers the sidelobe level of the TM03 mode. Multiport network modeling (MNM) of the structure is done, which leads to the optimum design, and the effect of curvature on antenna performance is also studied. As the antenna has around 60° beamwidth in one of the planes for both the frequency bands, three such antennas are placed on one arm of a quadcopter UAV so that 180° coverage can be obtained by switching the antennas. The antenna system has a simulated gain of 7.94 and 10.21 dBi at 10.288 and 12.412 GHz, respectively. A single slot-loaded cylindrical–rectangular patch antenna is fabricated and the measured results are compared with those obtained by MNM and simulation. The antenna gives a gain of 7.46 and 9.83 dBi at the frequencies of 10.323 and 12.46 GHz, respectively.
Q-learning Based Obstacle Avoidance Control of Autonomous Underwater Vehicle with Binocular Vision

Autonomous Underwater Vehicle (AUV) is widely used in various marine tasks, in which the autonomous obstacle avoidance function is the key technology to achieve autonomous motion such as path planning and trajectory tracking. Autonomous obstacle avoidance requires AUV to effectively detect obstacle information and select the optimal control strategy through autonomous judgment to achieve the obstacle avoidance function. However the ocean environment is very complex, full of uncertainty and danger, the underwater information obtained by using acoustic detection technology is very limited, which makes the control of underwater robot very difficult. To address this problem, this paper proposes a method for obstacle avoidance by binocular visual parallax control (BVPC) technique. In addition, the motion control of the robot is realized by Q learning (QL). In particular, the control strategy can be applied to different underwater unknown scenarios instead of a specific one, improving the wide applicability of this control method. Finally, the effectiveness of the proposed scheme is verified by simulation.
A CNN-based Approach for Multi-Classification of Brain Tumors

Early diagnosis of brain tumor plays an important factor in extending the life expectancy of a patient. Therefore, an accurate and timely diagnosis of the type of brain tumor will allow adequate treatment planning and medical assistance. Radiologists commonly use magnetic resonance imaging (MRI) scans to detect and classify brain tumors. The current methods used in the medical field for diagnosis are time-consuming and prone to human error. In recent years, researchers have developed automated techniques for the segmentation and classification of MRI images resulting in a faster diagnosis process. Recent advancements in deep learning have shown greater efficiency in image recognition and classification tasks. In this paper, a convolutional neural network (CNN) (a widely used deep learning architecture for image classification tasks) is developed to classify MRI images into four brain tumor categories. Data augmentation is applied to the training dataset to generalize the images and avoid overfitting problem. Additionally, this paper compares the performance of various pre-trained models such as Vision Transformer (VIT), VGG19, ResNet50, Inception V3, and AlexNet50 with that of the proposed model. Each experiment then explores transfer learning techniques like fine-tuning and freezing layers. In the study, the proposed model yields the most efficient results with a classification accuracy of 94.72%.
Event-triggered Adaptive Neural Tracking Control of Nonlinear Systems with Discrete and Distributed Time-Varying Delays

This paper investigates the problem of adaptive tracking control for a class of uncertain nontriangular nonlinear system with discrete and distributed time-varying delays. By applying the integral mean value theorem, the difficulties arising from distributed time-time delays are solved well. Moreover, an event-triggered mechanism involving saturation is introduced to reduce communication burden and maintain the competitive control capability. By utilizing the Lyapunov-Razumikhin approach, backstepping technique, and the neural network (NN) approximation-based method, an event-triggered adaptive neural control scheme is proposed for such systems. The event-based controller can ensure that all the closed-loop signals are semi-global uniformly ultimately bounded (SGUUB) while the tracking error converges to a small neighbourhood of the origin. Finally, a simulation example is provided to illustrate the effectiveness and applicability of the proposed scheme.
A Hierarchical and Location-Aware Consensus Protocol for IoT-Blockchain Applications

Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating existing consensus protocols in many IoT systems limits scalability and leads to high computational cost and consensus latency. In addition, location-centric characteristics of many IoT applications paired with limited storage and computing power of IoT devices bring about more limitations, primarily due to the location-agnostic designs in blockchains. We propose a hierarchical and location-aware consensus protocol (LH-Raft) for IoT-blockchain applications inspired by the original Raft protocol to address these limitations. The proposed LH-Raft protocol forms local consensus candidate groups based on nodes’ reputation and distance to elect the leaders in each sub-layer blockchain. It utilizes a threshold signature scheme to reach global consensus and the local and global log replication to maintain consistency for blockchain transactions. To evaluate the performance of LH-Raft, we first conduct an extensive numerical analysis based on the proposed reputation mechanism and the candidate group formation model. We then compare the performance of LH-Raft against the classical Raft protocol from both theoretical and experimental perspectives. We evaluate the proposed threshold signature scheme using Hyperledger Ursa cryptography library to measure various consensus nodes’ signing and verification time. Experimental results show that the proposed LH-Raft protocol is scalable for large IoT applications and significantly reduces the communication cost, consensus latency, and agreement time for consensus processing.
Measuring the spins of accreting black holes

A typical galaxy is thought to contain tens of millions of stellar-mass black holes, the collapsed remnants of once massive stars, and a single nuclear supermassive black hole. Both classes of black holes accrete gas from their environments. The accreting gas forms a flattened orbiting structure known as an accretion disk. During the past several years, it has become possible to obtain measurements of the spins of the two classes of black holes by modeling the x-ray emission from their accretion disks. Two methods are employed, both of which depend upon identifying the inner radius of the accretion disk with the innermost stable circular orbit, whose radius depends only on the mass and spin of the black hole. In the Fe Kα method, which applies to both classes of black holes, one models the profile of the relativistically broadened iron line with a special focus on the gravitationally redshifted red wing of the line. In the continuum-fitting (CF) method, which has so far only been applied to stellar-mass black holes, one models the thermal x-ray continuum spectrum of the accretion disk. We discuss both methods, with a strong emphasis on the CF method and its application to stellar-mass black holes. Spin results for eight stellar-mass black holes are summarized. These data are used to argue that the high spins of at least some of these black holes are natal, and that the presence or absence of relativistic jets in accreting black holes is not entirely determined by the spin of the black hole.
Leader-Follower Formation Control for Fixed-Wing UAVs using Deep Reinforcement Learning

This paper studies a fundamental formation flight scenario for fixed-wing unmanned aerial vehicles (UAVs) based on the leader-follower guidance and control frame using deep reinforcement learning (DRL) method. Firstly, on the basis of typical path following guidance problem, this paper proposes a complete dynamics for fixed-wing vehicle formation tracking flight with both acceleration and angular rate control. The tracking error dynamics with respect to the Serret- Frenet frame is derived where the singularity problem is avoided. Secondly, DRL methods are further introduced to cope with the highly coupled nonlinear problem. Based on both original application and indirect modifications of error dynamics, the online learning environments are respectively constructed. Thirdly, the implementation and comparative analysis of both deep deterministic policy gradient (DDPG) and deep Q-network (DQN) methods for solving the formation control problem are provided using deep neural network (DNN) approximation. Finally, the learning and control results of both different models and diverse DRL methods are given to verify the efficiency and applicability.
Distributed Finite-Time Secondary Frequency Control of an Islanded Microgrid with Renewable Energy Sources

This paper investigates a distributed control scheme for the coordination of an islanded microgrid considering renewable energy sources (RES). A distributed finite-time secondary frequency controller is designed to compensate the frequency deviation in a finite-time manner. The model of an islanded microgrid considering RES is presented with the connection of graph theory. Comparing with the traditional dynamic model, this paper gives a specific model of dc power source considering RES. Further, the design process of the distributed secondary frequency controller is described in detail, including the upper bounds of the convergence time. Finally, a simulation is provided to verify the effectiveness of our proposed method.
Range-aided Localization in Noisy Wireless Sensor Networks

This paper proposes a localization framework for range-aided single-robot or multi-robot systems. The proposed lo-calization framework can fuse different kinds of measurements for trajectory estimation. The proposed localization framework is not only model-free but also suitable for single channel communication systems. The designed sliding trajectory window reduces the computation such that the proposed localization framework is applicable to ultra-low power systems. Based on the proposed framework, the range-only-based pose estimation and range-velocity-based relative localization are presented. Moreover, the proposed framework is extended to the WiFi-based localization. The simulation and experimental results demonstrate the good localization performance of the proposed method.
Optimization of Asymmetric Timetable in Urban Rail Transit Under the Scenario of Tidal Passenger Flow

In urban rail transit, timetable optimization is an important approach to increase energy efficiency and improve passenger experience. With a limited number of inventory trains, it is a challenge to design an asymmetric timetable to match the train flow with the passenger flow while conserving energy as much as possible. In this work, a timetable-based state equation framework is developed and a nonlinear multi-objective optimization problem is established for energy efficiency and passenger experience. A case study illustrates that incorporating passenger and energy flow into train flow optimization helps to improve energy efficiency and passenger experience.
Interprocedural path profiling and the interprocedural express-lane transformation

The contributions of this thesis can be broadly divided into two categories: we present novel path-profiling techniques, and we present techniques for performing the express-lane transformation, a program transformation that duplicates frequently executed paths in the hope that better data-flow facts result along those paths. In path profiling, a program is instrumented with code that counts the number of times particular finite-length path fragments of the program's control flow graph are executed. This thesis presents a number of extensions to the intraprocedural path-profiling technique of Ball and Larus. Several of our techniques collect information about interprocedural paths (i.e., paths that cross procedure boundaries). We show that the overhead of our techniques is not prohibitive (300–700%), and that they often capture more information than the Ball-Larus technique. The express-lane transformation isolates and duplicates hot paths in a program, aiming for better data-flow facts along the duplicated path. We describe several variants of the interprocedural express-lane transformation, each of which duplicates hot paths from an interprocedural path profile. We show that an interprocedural express-lane transformation helps range analysis to determine the outcome of 0–7% more branches than the intraprocedural express-lane transformation and 1.5–19% more branches than performing no transformation. Code growth is one drawback of the express-lane transformation. When a pair of duplicate control-flow vertices have the same data-flow facts, it is desirable to eliminate one of the vertices (e.g., by coalescing the duplicate vertices). We present several effective techniques for eliminating duplicated code that has a redundant data-flow solution; this helps to control code growth. We also present experimental results for program optimizations that are based on: (1) performing an express-lane transformation; (2) performing range analysis; and (3) replacing decided branches and constant expressions. We show that when used with the intraprocedural express-lane transformation, this strategy leads to larger performance benefits than previously reported (0.7–13.0%). Using the interprocedural express-lane transformation also leads to performance benefits, although usually not enough to offset the costs incurred by the transformation. It is likely that a better implementation would lower these costs, possibly leading to a net performance gain.
Nonlinear dead water resistance at subcritical speed

The dead water resistance F1=12CdwρSU2 (ρ fluid density, U ship speed, S wetted body surface, Cdw resistance coefficient) on a ship moving at subcritical speed along the upper layer of a two-layer fluid is calculated by a strongly nonlinear method assuming potential flow in each layer. The ship dimensions correspond to those of the Polar ship Fram. The ship draught, b0, is varied in the range 0.25h0–0.9h0 (h0 the upper layer depth). The calculations show that Cdw/(b0/h0)2 depends on the Froude number only, in the range close to critical speed, Fr = U/c0 ∼ 0.875–1.125 (c0 the linear internal long wave speed), irrespective of the ship draught. The function Cdw/(b0/h0)2 attains a maximum at subcritical Froude number depending on the draught. Maximum Cdw/(b0/h0)2 becomes 0.15 for Fr = 0.76, b0/h0 = 0.9, and 0.16 for Fr = 0.74, b0/h0 = 1, where the latter extrapolated value of the dead water resistance coefficient is about 60 times higher than the frictional drag coefficient and relevant for the historical dea.
Multidimensional Scaling: Infinite Metric Measure Spaces

Multidimensional scaling (MDS) is a popular technique for mapping a finite metric space into a low-dimensional Euclidean space in a way that best preserves pairwise distances. We study a notion of MDS on infinite metric measure spaces, along with its optimality properties and goodness of fit. This allows us to study the MDS embeddings of the geodesic circle $S^1$ into $\mathbb{R}^m$ for all $m$, and to ask questions about the MDS embeddings of the geodesic $n$-spheres $S^n$ into $\mathbb{R}^m$. Furthermore, we address questions on convergence of MDS. For instance, if a sequence of metric measure spaces converges to a fixed metric measure space $X$, then in what sense do the MDS embeddings of these spaces converge to the MDS embedding of $X$? Convergence is understood when each metric space in the sequence has the same finite number of points, or when each metric space has a finite number of points tending to infinity. We are also interested in notions of convergence when each metric space in the sequence has an arbitrary (possibly infinite) number of points.
Astrophysical applications of gravitational microlensing

Since the first discovery of microlensing events nearly two decades ago, gravitational microlensing has accumulated tens of TBytes of data and developed into a powerful astrophysical technique with diverse applications. The review starts with a theoretical overview of the field and then proceeds to discuss the scientific highlights. (1) Microlensing observations toward the Magellanic Clouds rule out the Milky Way halo being dominated by MAssive Compact Halo Objects (MACHOs). This confirms most dark matter is non-baryonic, consistent with other observations. (2) Microlensing has discovered about 20 extrasolar planets (16 published), including the first two Jupiter-Saturn like systems and the only five “cold Neptunes" yet detected. They probe a different part of the parameter space and will likely provide the most stringent test of core accretion theory of planet formation. (3) Microlensing provides a unique way to measure the mass of isolated stars, including brown dwarfs and normal stars. Half a dozen or so stellar mass black hole candidates have also been proposed. (4) High-resolution, target-of-opportunity spectra of highly-magnified dwarf stars provide intriguing “age" determinations which may either hint at enhanced helium enrichment or unusual bulge formation theories. (5) Microlensing also measured limb-darkening profiles for close to ten giant stars, which challenges stellar atmosphere models. (6) Data from surveys also provide strong constraints on the geometry and kinematics of the Milky Way bar (through proper motions); the latter indicates predictions from current models appear to be too anisotropic compared with observations. The future of microlensing is bright given the new capabilities of current surveys and forthcoming new telescope networks from the ground and from space. Some open issues in the field are identified and briefly discussed.