Miracle-dz/UniFAR-FTUs huggingface dataset:
Photovoltaic Inverter Efficiency and Lifetime Trade-off using Model based Real-Time Optimization

Lifetime of the photovoltaic (PV) inverters is influenced by its power profile. The reliability of such PV inverters is affected by the thermal fatigue cycles witnessed by the underlying components. However, there is a trade-off between the inverter efficiency and the fatigue witnessed by its components. With a systematic formulation of this trade-off, a real-time nonlinear optimization problem is formulated to generate the appropriate reactive power set-points to the PV inverter controller. The proposed approach improves the lifetime of the inverters while keeping its efficiency above desired threshold value. The time domain loss and damage models that uses PV power profile as an input are critical to the proposed optimization framework. The proposed framework provides an option to the customer to operate the PV inverter with an objective of lifetime improvement under the acceptable losses by flattening the component thermal fatigue cycles. The framework is evaluated using the PV power profile of a 10 kVA PV inverter with various simulated case studies.
Argumentation and Dialogue

This chapter contains sections titled: 17.1 Dialogue games and actual debate, 17.2 Learning by playing, 17.3 Constructive versus classical logic, 17.4 The logic of the games, 17.5 Extensions and variations, 17.6 Conclusion, 17.7 Literature, 17.8 Appendix on argumentation
Point Contrastive learning for LiDAR-based 3D object detection in autonomous driving

Current progress in 3D Perception tasks for autonomous driving relies upon neural network architectures that their training requires a growing demand for annotated data. However, semantic annotation of 3D scenes is a very expensive and labor-intensive task. In this paper, we present an approach for self-supervised, data-efficient learning in the context of point contrastive learning, using two distinct pre-training techniques towards improving performance in LiDAR-based 3D object detection in autonomous driving. Our experimental work relies upon standard benchmarking datasets, namely KITTI and Waymo. Under a comprehensive evaluation framework it is shown that, in the absence of large annotated data, the proposed approach could achieve improved performance.
A Distributed Time-Varying Optimization Algorithm For Networked Lagrangian Agents Generating Continuous Control Torques

In this paper, the distributed time-varying optimization problem is investigated for networked Lagrangian systems with parametric uncertainties. Due to the usage of the signum function in the control torque design, there might exist chattering while implementing the distributed time-varying optimization algorithms for networked Lagrangian agents in the existing works. To this end, we design a distributed optimization algorithm that is capable of generating continuous control torques and achieving exact optimum tracking. A simulation is presented to validate the effectiveness of the proposed algorithm.
Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks

Model Free Reinforcement Learning (MFRL) has shown significant promise for learning dexterous robotic manipulation tasks, at least in simulation. However, the high number of samples, as well as the long training times, prevent MFRL from scaling to complex real-world tasks. Model- Based Reinforcement Learning (MBRL) emerges as a potential solution that, in theory, can improve the data efficiency of MFRL approaches. This could drastically reduce the training time of MFRL, and increase the application of RL for real- world robotic tasks. This article presents a study on the feasibility of using the state-of-the-art MBRL to improve the training time for two real-world dexterous manipulation tasks. The evaluation is conducted on a real low-cost robot gripper where the predictive model and the control policy are learned from scratch. The results indicate that MBRL is capable of learning accurate models of the world, but does not show clear improvements in learning the control policy in the real world as prior literature suggests should be expected.
Learning Food Picking without Food: Fracture Anticipation by Breaking Reusable Fragile Objects

Food picking is trivial for humans but not for robots, as foods are fragile. Presetting foods' physical properties does not help robots much due to the objects' inter- and intra-category diversity. A recent study proved that learning-based fracture anticipation with tactile sensors could overcome this problem; however, the method trains the model for each food to deal with intra-category differences, and tuning robots for each food leads to an undesirable amount of food consumption. This study proposes a novel framework for learning food-picking tasks without consuming foods. The key idea is to leverage the object-breaking experiences of several reusable fragile objects instead of consuming real foods while making the picking ability object-invariant with domain generalization (DG). In real-robot experiments, we trained a model with reusable objects (toy blocks, ping-pong balls, and jellies), selected based on the three common fracture types (crack, rupture, and crush). We then tested the model with four real food objects (tofu, bananas, potato chips, and tomatoes). The results showed that the proposed combination of reusable objects' breaking experiences and DG is effective for the food-picking task.
Infrared Image Combination Expansion Method Based on Supervised Single Sample Power Equipment

Due to the particularity and confidentiality of the power industry, the infrared images of power equipment do not have a good public data set to use, and the collected sample data information is extremely unbalanced, and too little data can easily lead to overfitting of the network. In order to solve this problem, this paper finds a most suitable augmentation method for infrared images of power equipment based on the supervised single-sample image augmentation method. Firstly, the characteristics of common augmentation methods are analyzed, and then the five methods of noise, flipping, random cropping, scaling and contrast in the single-sample augmentation method are selected for detailed analysis according to the unique characteristics of infrared images of power equipment, and finally these five methods are verified based on VGG16 and ResNet50 classification models. Case analysis shows that the contrast, flipping and noise expansion methods have better expansion effects, and the effect of the double expansion combination is better than any single expansion method under the same amplification increment, among which contrast + flip and contrast + noise are the best combinations of the double combination methods, and the detection accuracy is improved by 6.17%, 5.90%, 0.98% and 0.69%, respectively.
Design of a Versatile Soft Pneumatic Gripper

Conventional robotic grippers are rigid mechanisms that use suction for grasping objects and are often not used in areas like the food/agriculture industries due to the highly varying sizes, shapes, and weights of the objects which should be handled by them. Since they are designed for specific use cases which are often well-known in advance they are not designed to handle requirements like fragility, deformity, etc. Soft robotic grippers are a solution to this. With the increase in human-robot interactions and increased usage of co-bots, the need for soft robotic grippers is also growing. but often they too are designed for specific needs only. This paper discusses the design of a soft pneumatic gripper with a variable fixture, which can change the grasping workspace area accordingly to make the gripper more versatile. This will enable the gripper to be used in various situations where a traditional gripper requires manual/automated end-effector swap. The finite element analysis of the soft pneumatic actuator and the structure is performed and the parts are fabricated. The bending action of the soft pneumatic actuator is evaluated.
On Wide-Area Control of Solar-Integrated DAE Models of Power Grids

Today’s power systems are controlled based on decades of experience with the fundamentals of physics-based properties of synchronous generators. Future power grids however must cope with the increasing penetration of renewable energy resources (RERs) and require a much more sophisticated control architecture. This is because RERs are formed by uncertain solar- and wind-based resources and are connected to the grid via advanced (power electronics)-based technologies. These are, in short, far more complex to control than traditional generators. RERs also do not provide inertia to damp frequency oscillations, and thus the grid’s operating point changes frequently causing deterioration in the overall transient stability of the power system. This short paper proposes a robust wide-area controller for an advanced power system model having a higher order generator model, advanced (power electronics)-based solar plants model, and composite load dynamics. The simulation studies show that the proposed controller can significantly improve the transient stability of the system against uncertainty from load demand and renewables.
Safe Control Using High-Order Measurement Robust Control Barrier Functions

We study the problem of providing safety guarantees for dynamic systems of high relative degree in the presence of state measurement errors. To this end, we propose High-Order Measurement Robust Control Barrier Functions (HO-MR-CBFs), an extension of the recently proposed Measurement Robust Control Barrier Functions. We begin by formally defining HO-MR-CBF, and identify conditions under which the proposed HO-MR-CBF can render the system’s safe set forward invariant. In addition, we provide bounds on the state measurement errors for which the optimization problem for identifying the corresponding safe controllers is feasible for all states within the safe set and given restricted control inputs. We demonstrate the proposed approach through numerical experiments on a collision avoidance scenario in presence of measurement noise. We show that using our proposed control method, the robot, which has access to only biased state estimates, will be successful in avoiding the obstacle.
Agent Based Simulation of Botnet Volumetric and Amplification Attack Scenarios Applied to Smart Grid Systems

All industries rely on smart grid infrastructures and systems to energy systems to provide power supply to industries and individual users for innovation, economic growth and sustainability as part of SGD goals. However, recent attacks on the smart grid using various attack methods have made it inevitable to provide security implementation for sustainable development infrastructures and economic growth. Agent-based simulation (ABS) considers modelling complex adaptive systems in a heterogeneous environment to detect their interactive behaviours and attacks. Agents can represent people, households, and business entities in a smart grid system. ABSs are created with three core attributes, the declaration of the agent's architectures and associated agent classes, an agent environment, and the software modules to establish communication protocols between agents. However, threat actors can use these attributes to cause Distributed Denial of Service (DDoS) and False Data Injection Attacks (FDIA) on the smart grid. The paper presents an agent-based simulation of offensive botnet interactions within a smart grid system and considers amplification attack scenarios of DDoS and FDIA on the smart grid. The contribution of the paper is threefold. First, we explore how botnet agent attacks systems using ABS impact of cooperative defence during DDoS and FDIA attacks. Secondly, we implement attack models using GAMA tool to determine offensive botnet interactions within a smart grid system. Finally, we recommend control mechanisms to prevent offensive botnets on the smart grid network. The results show that ABS could be used to detect offensive botnet interactions within smart grid systems to improve cybersecurity.
Grid Frequency Trajectory Fitting under Transient Power Impact Caused by Highly Proportional Wind Power Fault Ride

With the continuous increase of wind power penetration, the temporary power shortage generated by the low-voltage ride-through (LVRT) characteristics of wind power will bring a huge impact on the power system, even triggering a rapid drop in grid frequency and the risk of under-frequency load-shedding (UFLS), and it is necessary to study the rapid prediction and fitting technology of frequency trajectory under the temporary power shock. The active power recovery curves of wind turbines with different LVRT strategies show different patterns, and if the power recovery process is ignored to directly resolve this power disturbance as a permanent power disturbance or step disturbance superposition, it will cause a huge error in frequency evaluation. To solve the problem above, a system power disturbance resolution method, which takes the power recovery process into account, is proposed to resolve the active power disturbances in the LVRT, recovery, and normal periods of wind power using segmentation functions. Then, an optimized system frequency response (SFR) equivalent model is proposed considering the frequency modulation (FM) and virtual inertia of wind power; based on the equivalent power principles, the frequency response (FR) transfer function of the equivalent wind turbine considering multiform LVRT trajectories can be parametrically identified and the frequency trajectory will be fitted or predicted quickly. Finally, the accuracy and effectiveness of the proposed method are verified through the simulation of the IEEE 39-bus system in the case study.
Dynamic Locomotion of Quadruped Robot in Sloping Terrain

In order to improve the locomotion capability of quadruped robot in the sloping terrain and enhance whose adaptability to the environment, this paper proposes a method to realize dynamic locomotion of quadruped robot in sloping terrain completely based on model predictive control (MPC) and proprioception. According to the foot contact signal and the robot body posture information measured by inertial measurement unit (IMU), quadruped robot actively changes whose body posture to adapt to different sloping terrains. This paper builds quadruped robot virtual prototype and test terrain in Webots simulation environment. The experimental results show that with this method, the roll angle fluctuation range of the quadruped robot is less than 0.05rad and the yaw angle fluctuation range is not more than 0.003rad. At the same time, when the angle of slope changes, the terrain adaptation time of the quadruped robot is less than 1.8s. The above results show that quadruped robot has excellent movement ability in sloping terrain and environmental adaptability under the condition of ensuring stability with the method proposed in this paper.
Trajectory Optimization for Distributed Manipulation by Shaping a Physical Field

Trajectory optimization is used to solve various planning tasks. In this paper we present a optimization-based method that solves a planning problem for multiple independent objects manipulated by a spatially continuous physical field. The field is generated and controlled (shaped) in real time by an array of actuators. In the paper we first formulate a trajectory optimization problem and a related initialization scheme, and then we demonstrate the proposed method using an experimental platform for distributed magnetic manipulation. The demonstrated task is that of planar reconfiguration of an ensemble of multiple objects, which significantly benefits from the inherent parallelism of the manipulation enabled by the array of actuators shaping the physical field. We show that the system can rearrange up to eight objects simultaneously while avoiding collisions.
Data-Driven Stability Certificate of Interconnected Homogeneous Networks via ISS Properties

This letter is concerned with a compositional data-driven approach for stability certificate of interconnected homogeneous networks with (partially) unknown dynamics while providing 100% correctness guarantees (as opposed to probabilistic confidence). The proposed framework enjoys input-to-state stability (ISS) properties of subsystems described by ISS Lyapunov functions. In our data-driven scheme, we first reformulate the corresponding conditions of ISS Lyapunov functions as a robust optimization program (ROP). Due to appearing unknown dynamics of subsystems in the constraint of ROP, we propose a scenario optimization program (SOP) by collecting data from trajectories of each unknown subsystem. We solve SOP and construct an ISS Lyapunov function for each subsystem with unknown dynamics. We accordingly leverage a compositional technique based on $\max $ -type small-gain reasoning and construct a Lyapunov function for an unknown interconnected network based on ISS Lyapunov functions of individual subsystems. We demonstrate the efficacy of our data-driven approach over a room temperature network containing 1000 rooms with unknown dynamics. Given collected data from each unknown room, we verify that the unknown interconnected network is globally asymptotically stable (GAS) with 100% correctness guarantee.
Preliminary Evaluation of a Wearable Thruster for Arresting Backwards Falls

This paper presents preliminary results assessing the efficacy of a backpack-worn cold-gas thruster to potentially arrest impending backwards falls. Specifically, a nitrogen-based cold gas thruster system was integrated into a backpack-worn prototype device, and experiments were conducted to assess the effect of the wearable device on backwards falls. Although the device is eventually intended for individuals at fall risk, these preliminary experiments were conducted on three healthy subjects. The experiments compared each subject's ability to recover from an impending fall with and without assistance from the thruster. Results suggest that the likelihood of a fall was substantially reduced with the thruster assistance.
ROLE OF SKELETAL MUSCLE MASS IN SEX-DEPENDENT POWER OUTPUT DURING FLYWHEEL RESISTANCE TRAINING

OF THESIS Background: To determine the role of muscle mass in sex-dependent differences in power output during flywheel resistance training (FRT). Methods: Twenty recreationally active (≥ 2 resistance exercise bouts per week), subjects (10 M, 10 F) completed 2 bouts of resistance exercise using a flywheel resistance training (FRT) device (Exxentric kbox 4 Pro) separated by at least one week. Each session consisted of 3 sets of 4 exercises (squat, bent-over row, Romanian deadlift, and biceps curl) with varying moments of inertia (0.050, 0.075, and 0.100 kg/m2, respectively) in random order. Each set consisted of 5 maximal effort repetitions with 3-minute recovery between sets. Average power, peak concentric and peak eccentric power were recorded and normalized to whole-body skeletal muscle mass (as calculated from bioelectrical impedence analysis). Additionally, linear regression analysis was used to determine the association between muscle mass and highest power output observed among all three inertial loads. Results: Absolute average, peak concentric and peak eccentric power for all lifts was significantly higher for males compared to females except for peak eccentric power for biceps curl which showed no significant difference. After normalizing to skeletal muscle mass, power output remained significantly higher for men in Row average power and peak concentric power as well as average power for biceps curl. A significant main effect of inertial load was noted for both absolute and relative power output for all exercises except for squat average power and peak concentric power. Regression analysis revealed that power output increases linearly with skeletal muscle mass (R2 = 0.37-0.77). Conclusions: Differences in power output between sexes during resistance exercise can largely be explained by differences in muscle mass. Indeed, muscle mass accounts for approximately 37-77% of the variance in power output during FRT depending on the exercise. Increasing inertial load tends to decrease power output during FRT.
Measurement of Noise Correlation Through Superconducting Josephson junctions

We demonstrate that it is possible to measure the time correlation of a Gaussian noise through the analysis of the escape times of Josephson junctions. To do so we construct the histogram – through Kernel estimation, of the collected data and we observe that the structure depends upon the duration of the correlation in noise. This paves the way towards the possibility to measure in situ noise correlation with a superconducting device.
Forecasting the Market: A Survey of Techniques for Stock Market Prediction

Investors who buy and sell stocks need current information to make informed choices. The stock exchange trades various stocks, and the decision-making process is affected by multiple factors. Predicting stock values is difficult due to their volatile nature. In this paper, we categorize the stock market prediction strategies according to the input data and the techniques used. The major works that explored the area of stock market prediction are discussed and their challenges are identified. Different techniques used for stock prediction and itâs limitations are studied. Various evaluation metrics used by researchers are also identified. Incorporating financial news, stock forum data, and social media sentiments can further improve the current stock prediction methods that use historical data or technical indicators only.
Combining Scene Coordinate Regression and Absolute Pose Regression for Visual Relocalization

Visual relocalization is a fundamental problem in computer vision and robotics. Recently, regression-based methods become popular and they can be categorized into two classes: absolute pose regression and scene coordinate regression. In this work, we present a combined regression network that jointly learns scene coordinate regression and absolute pose regression for single-image visual relocalization. The proposed network composes of a feature encoder and two regression branches with uncertainty modeling. In particular, we design a deep feature conditioning module, aiming at propagating the coarse pose information in absolute pose regression to inform the predictions in scene coordinate regression. The proposed network is trained in an end-to-end fashion to learn both regression tasks. Moreover, we propose an uncertainty-driven RANSAC algorithm that incorporates the predicted scene coordinates and their uncertainties to solve the camera pose during inference. To the best of our knowledge, this work is the first to combine scene coordinate regression and pose regression in a hierarchical framework for visual relocalization. Experiments on indoor and outdoor benchmarks demonstrate the effectiveness and the superiority of the proposed method over the state-of-the-art methods.