Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Q-Network Based Target Search of UAV under Partially Observable Conditions

Autonomous target search for unmanned aerial vehicles (UAVs) has broad application scenarios in both military and civilian domains. In this paper, In this paper, a deep reinforcement learning-based autonomous target search method for the USV is proposed which incorporates the recurrent neural network to achieve accurate decision-making under partially observable conditions. In our study, firstly, we model the UAV’s target search task as a partially observable Markov decision process problem and use the Deep Q-Network(DQN) to evaluate the current state and generate actions. Secondly, the gated recurrent unit (GRU) is introduced to mitigate the decision bias that occurs in the UAV under incompletely observable conditions and to achieve a mapping from incomplete to more complete perceptual information of the UAV at the current moment. Finally, to avoid the problem of slow convergence of the algorithm caused by sparse rewards, we combine prior knowledge to design reward functions suitable for the UAV target tracking task to facilitate faster convergence of the algorithm. To verify the effectiveness of the algorithm, we designed an experimental environment for UAV target search based on a 3D simulation platform. The experimental results show that our method enables the UAV to successfully search for targets in less time for the same training episodes.
Hybrid Learning System for Large-scale Medical Image Analysis

Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions.
Advanced Analytics in Central Banks: Basic Assumptions and Preliminary Results of a Research Project

: This position paper presents methodological considerations and ideas as well as preliminary results of a research project. We argue that integrating design science (DS) and review research is useful for developing artifacts that serve, for example, to share knowledge within a domain. Our project aims to support knowledge sharing in central banking by means of systematic reviews. Such a consolidated body of knowledge can also be considered an artifact in the sense of DS research. We demonstrate how such a review can be conducted and what results it yields. Since it is not clear whether such an application of systematic reviews is relevant and feasible, problem definition and objectives of the project are discussed.
On the mass modelling in vibration analysis of thin-walled structures using the finite element method

Modelling of mass in vibration analysis of thin-walled structures using the finite element method has been considered, i.e. consistent and lumped mass matrices have been compared to a newly introduced, simplified mass matrix. The matrices have been specified for a two-noded beam element, three-noded triangular plate element and four-noded rectangular plate element. The simplified mass matrix has been derived on the basis of bar and membrane shape functions instead of the bending ones. As a result, the distributed mass has been considered only to the deflectional degree of freedom. Accuracy and advantage of such a mass matrix formulation is illustrated for the cases of beam and square plate vibrations.
Learning Human-like Driving Policies from Real Interactive Driving Scenes

: Traffic simulation has gained a lot of interest for autonomous driving companies for qualitative safety evaluation of self driving vehicles. In order to improve self driving systems from synthetic simulated experiences, traffic agents need to adapt to various situations while behaving as a human driver would do. However, simulating realistic traffic agents is still challenging because human driving style cannot easily be encoded in a driving policy. Adversarial Imitation learning (AIL) already proved that realistic driving policies could be learnt from demonstration but mainly on highways (NGSIM Dataset). Nevertheless, traffic interactions are very restricted on straight lanes and practical use cases of traffic simulation requires driving agents that can handle more various road topologies like roundabouts, complex intersections or merging. In this work, we analyse how to learn realistic driving policies on real and highly interactive driving scenes of Interaction Dataset based on AIL algorithms. We introduce a new driving policy architecture built upon the Lanelet2 map format which combines a path planner and an action space in curvilinear coordinates to reduce exploration complexity during learning. We leverage benefits of reward engineering and variational information bottleneck to propose an algorithm that outperforms all AIL baselines. We show that our learning agent is not only able to imitate humane like drivers but can also adapts safely to situations unseen during training.
Evaluation of the seismic performance of isolated electrical transformers under pulse-like excitations

: Damage sustained by electrical transformers in past strong earthquakes led to irrecoverable and severe economic losses. The seismic performance evaluation is associated with the loss of proper functioning of transformers. This study deals with modeling existing isolated electrical transformer structures to evaluate the effects of variables that may affect the seismic performance and dynamic characteristics. The results probabilistically determine the seismic performance acceptability of study isolated electrical transformer structures based on the impact of key structural response parameters on the seismic performance of the transformer. Analyses of systems for a wide range of parameters are performed. The effects of horizontal and vertical near-fault pulse-like ground motions, the displacement capacity of the seismic isolation system, limit states of electrical bushings, and details of the isolation system design are considered. Also, the probability of failure of the transformer under the near-fault excitations with pulse-like characteristics is investigated. The results of the research showed that the three-dimensional seismic isolation system has a significant effect on improving the seismic performance of the system for a large number of parameters and can be further effective compared with horizontal-only seismic isolation, offering the lowest probabilities of failure for all cases of transformer and isolation system parameters.
Optimum design plan of constant stress life testing for exponentiatedLomaxdistribution based on time censoring

92 Abstract The customers' hopes are having great superiority reliable goods on period and reasonable costs. Then, researchers spend accelerated life testing that allows rapidly them reduction the examination period and keep a group of manpower, money and material sources. The major aim of this paper is to study the constant stress time censoring accelerated life tests. It is supposed that the lifetime of a test unit monitors an exponentiated lomax distribution. The estimators of maximum likelihood are developed when the parameters are unknown. The shape parameter of the lifetime distribution at constant stress stages is adopted to be a log linear model. Furthermore, confidence limits of intervals for the parameters, reliability, hazard functions are formed. Moreover, optimum constant acceleration life testing plan is studied through the asymptotic generalized variance of the maximum likelihood estimators of the parameters is minimized. Monte Carlo simulation and two real data are approved to indicate the theoretical results.
Identification of Potential Location and Extent of Localized Interface Debonding in the Wheelpath of Asphalt Pavements

Debonding between asphalt layers is usually modeled as a global or “smeared” phenomenon across the entire lane. However, field evidence indicates debonding may be limited to a portion of the interface. The objective of this study was to determine the potential location and extent of localized interface debonding in asphalt pavements so that more realistic interface conditions and pavement responses can be employed in future performance predictions. A parametric study was conducted to locate stress states potentially conducive to interface debonding. Factors considered included asphalt concrete (AC) layer thickness, AC-to-base stiffness ratio, interface compliance, tire size, and traffic wander. The parametric study showed existence of a zone of high shear stress coupled with low confinement for a broad range of depths (1–3 in. below the surface) and extending to 2 in. from the tire edge. Given the drop in confinement immediately outside the tire edge and that shear stress magnitude in this zone was similar to shear strength values reported in the literature, it was concluded that the repetition of these critical stress state conditions can cause localized debonding of an interface located about 2 in. below the pavement surface. Existence of a potential zone of localized interface debonding around the edge of a tire can promote a debonded strip below the wheelpath, which is consistent with field observations. The width of the debonded strip can extend to 42 in. Future research efforts should examine the stress redistribution associated with the presence of a debonded strip below the wheelpath.
Principal Component Analysis in Gas Transport Simulation

: In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulﬁllment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or ﬂows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
Using proximity in sustainable product design

This research examines proximity as a new interesting strategy to include in the design of more sustainable products. Drawing from the construal level theory, we posit that the environmental sustainability of a product embedding a form of proximity to an environmental solution in its design will be perceived as more concrete and will trigger higher prosocial product experience. To test this assumption, we used spatial proximity by manipulating the location from where the recycled plastic of a bottle of dishwashing soap was reclaimed. Based on the responses of 130 individuals recruited from a panel of consumers, we found that product environmental sustainability is perceived as more concrete and prosocial product experience is higher when proximity is embedded in product design than when far distance or no distance are embedded in the product design. This paper contributes by investigating how product design itself can help to enhance the acceptance of more sustainable products and by applying the Construal Level Theory to the field of product design.
Grape: Grammar-Preserving Rule Embedding

Word embedding has been widely used in various areas to boost the performance of the neural models. However, when processing context-free languages, embedding grammar rules with word embedding loses two types of information. One is the structural relationship between the grammar rules, and the other one is the content information of the rule definition. In this paper, we make the first attempt to learn a grammar-preserving rule embedding. We first introduce a novel graph structure to represent the context-free grammar. Then, we apply a Graph Neural Network (GNN) to extract the structural information and use a gating layer to integrate content information. We conducted experiments on six widely-used benchmarks containing four context-free languages. The results show that our approach improves the accuracy of the base model by 0.8 to 6.4 percentage points. Furthermore, Grape also achieves 1.6 F1 score improvement on the method naming task which shows the generality of our approach.
Silicone-Based Molding Technique for Optical Flow Analysis in Transparent Models of Fluidic Components

Optical flow analysis methods such as particle image velocimetry can only be performed in fluid systems or components with optical access. Many fluidic components, such as metallic tubes, do not typically feature optical accessibility. A new silicone-based molding technique is presented that makes it possible to replicate non-transparent fluidic components as models with optical accessibility that can be used to perform optical flow analysis. Furthermore, to avoid errors due to refraction, a test fluid is presented whose refractive index matches that of the silicone material of the replication. This new method allows flows to be analyzed in tubes or similar components with diameters in the range of only a few millimeters. An initial test was performed demonstrating the proof of concept and the velocity field of the flow inside a manifold was measured using a micro-particle image velocimetry setup. The study showed that both simple parts like tubes and complex parts such as manifolds can be replicated and investigated.
A Novel Frequency Scanning Leaky-Wave Antenna Based on Corrugated Substrate Integrated Waveguide

In order to facilitate the integration of active devices, a leaky-wave antenna (LWA) based on corrugated substrate integrated waveguide (CSIW) structure is proposed. By introducing rectangular ring slots on the CSIW structure, the proposed LWA is capable of beam scanning from backward to forward. The rectangular ring slots do not only enable energy leaking but also provide series capacitance. In addition, the grounded vias and quarter-wave open-circuit stubs provide shunt inductance. Thus, the proposed CSIW exhibits left-handed characteristic which enables beam scanning through the broadside. To further facilitate the design and suppress open-stopband of beam scanning antenna based on CSIW structure, an equivalent circuit model is developed and the relationships between structure parameters and the equivalent circuit parameters are presented. Based on this design guide, a leaky wave antenna is designed with radiation angle ranging from −53° to 23°. This proposed novel CSIW LWA has great potential in tunable or reconfigurable antenna design due to its ease of integration with active devices.
Large Neighborhood Search with Decision Diagrams

Local search is a popular technique to solve combinatorial optimization problems efficiently. To escape local minima one generally uses metaheuristics or try to design large neighborhoods around the current best solution. A somewhat more black box approach consists in using an optimization solver to explore a large neighborhood. This is the large-neighborhood search (LNS) idea that we reuse in this work. We introduce a generic neighborhood exploration algorithm based on restricted decision diagrams (DD) constructed from the current best solution. We experiment DD-LNS on two sequencing problems: the traveling salesman problem with time windows (TSPTW) and a production planning problem (DLSP). Despite its simplicity, DD-LNS is competitive with the state-of-the-art MIP approach on DLSP. It is able to improve the best known solutions of some standard instances for TSPTW and even to prove the optimality of quite a few other instances.
Designing Naturalistic Simulations for Evolving AGI Species

: This paper identifies basic principles for designing and creating evolutionary simulations in the context of general-purpose AI (AGI). It is argued that evolutionary simulations which employ certain nature-inspired principles can be used to evolve increasingly intelligent AGI species. AGI frameworks are particularly suited for evolutionary experiments involving embodiment since they can operate arbitrary evolved bodies. Once a designer manually defines a simulation’s initial conditions, each run is an automated exploration of a novel subset of species. In this way, naturalistic simulations generate huge amounts of empirical data for evaluating the robustness of AGI frameworks, along with many promising species that can be later instantiated in other simulated environments or even physical robots for practical applications.
Divisiveness, splintering, and the rational interpretation of text

In a historical case study, this paper explores the mechanisms by which the rational interpretation of written text can produce divisiveness and splintering. The mechanisms of division are derived from theories of organizational rationality, cultural logics, organizational memory, and group conflict. The propositions are explored in an analysis of common sense rationality with a focus on the 19th century Stone-Campbell movement. When emotions are not adequately integrated into the culture of rational interpretation, group conflict can arise. The material characteristics of written text combined with a high-diffusion, low-hierarchy, highly recorded and publicized, and proactively contentious, emotionally triggering environment creates a breeding ground for division. The analysis paints a multi-dimensional picture of division and splintering from a sociomaterial standpoint.
An Intrinsic Human Physical Activity Recognition from Fused Motion Sensor Data using Bidirectional Gated Recurrent Neural Network in Healthcare

: An intrinsic bi-directional gated recurrent neural network for recognising human physical activities from intelligent sensors is presented in this work. In-depth exploration of human activity data is significant for assisting different groups of people, including healthy, sick, and elderly populations in tracking and monitoring their level of healthcare status and general fitness. The major contributions of this work are the introduction of a bidirectional gated recurrent unit and a state-of-the-art nonlinearity function called rectified adaptive optimiser that boosts the performance accuracy of the proposed model for the classification of human activity signals. The bidirectional gated recurrent unit (Bi-GRU) eliminates the short-term memory problem when training the model with fewer tensor operations, and the nonlinear function, a variant of the classical Adam optimiser provides an instant dynamic adjustment to the adaptive models’ learning rate based on the keen observation of the impact of variance and momentum during training. A detailed comparative analysis of the proposed model performance was conducted with long-short-term-memory (LSTM), gated recurrent unit (GRU), and bi-directional LSTM. The proposed method achieved a remarkable landmark result of 99% accuracy on the test samples, outperforming the earlier architectures.
A Virtual Space Model based on Torque Gradient of IPMSMs with Nonlinear Characteristics

This paper proposes a virtual space model of IPMSMs with nonlinear characteristics to improve torque control performances. The nonlinear torque including a reluctance component, magnetic saturation, spatial harmonics, and variation of the magnetic energy degrade control performances. To solve this problem, we propose a virtual space model, which is obtained by transforming current and voltage spaces with virtual vectors. The virtual vectors are defined by gradients of a torque map on the d-q current plane of IPMSMs. The proposed virtual space is suitable for torque control because the basis vectors are based on the torque map. We applied the virtual space model to a sensorless torque feedback control system to confirm the validity of the proposed model. As a result of a simulation, it is verified that the maximum torque response under a voltage limitation and the Maximum Torque Per Ampere control are achieved by the virtual space model.
Zero voltage switching high step‐up boost converter with coupled inductor for renewable energy applications

This paper proposes a zero‐voltage switching high step‐up boost converter with coupled inductor for renewable energy applications. In this proposed circuit, main and auxiliary switches are turned ON at zero voltage switching to reduce switching losses. The voltage gain of the converter is increased by varying the turns ratio between primary and secondary of the coupled inductor (CI). The voltage spikes at the main switch are mitigated by using active clamping technique. The reverse recovery problem in diodes is alleviated due to leakage inductance of the CI. Zero current switching turn OFF in output diode is achieved, which can enhance the efficiency of the converter. A real‐time model for 100 W proposed converter circuit is developed to verify the theoretical analysis.
Ideal physical type and body image satisfaction of regular walkers

The objective of the present study was to quantify the ideal physical type and to verify the body image satisfaction level of regular walkers. One hundred and eighty six individuals participated in this study as follows: 87 women (age = 28.70 ± 12.6 years, stature = 161.6 ± 6.2 cm, body mass = 58.9 ± 12.0 kg, and fat = 25.7 ± 7.8 F%), and 98 men (age = 27.9 ± 12.9 years, stature = 177.2 ± 6.9 cm, body mass = 75.0 ± 12.3 kg, and fat = 13.3 ± 6.1 F%). The individuals were asked to indicate which profile corresponded to his/her body and which profile they wanted to reach. Only 24% of women are satisfied. Profile 3 was pointed as ideal to be reached by 55% of women (profile 2 = 18%; and profile 4 = 21%). Profile 3, according to results of this study, corresponded to F% of 20.5 ± 0.9% (EPM) and to MBI of 20.0 ± 0.3 kg/m2 (EPM). In relation to men, only 18% are satisfied. Profile 4 was pointed as ideal by 47% of men (profile 3 = 23%; and profile 5 = 19%). Profile 4 corresponds to F% of 9.8 ± 1.4% (EPM) and to BMI of 23.1 ± 0.4 kg/m2 (EPM). There is an ideal physical type for both genders. No difference with body image satisfaction degree between genders was verified.
Calibration of a 2D Scanning Radar and a 3D Lidar

: In search and rescue applications, mobile robots have to be equipped with robust sensors that provide data under rough environmental conditions. One such sensor technology is radar which is robust against low-visibility conditions. As a single sensor modality, radar data is hard to interpret which is why other modalities such as lidar or cameras are used to get a more detailed representation of the environment. A key to successful sensor fusion is an extrinsically and intrinsically calibrated sensor setup. In this paper, a target-less calibration method for scanning radar and lidar using geometric features in the environment is presented. It is shown that this method is well-suited for in-ﬁeld use in a search and rescue application. The method is evaluated in a variety of use-case relevant test scenarios and it is demonstrated that the calibration results are accurate enough for the target application. To validate the results, the proposed method is compared to a target-based state-of-the-art calibration method showing equivalent performance without the need for specially designed targets.