Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of a Moving Speed Enhancing Method for Peristaltic Motion Type In-Pipe Inspection Robot with Linear Antagonistic Mechanism Using Endoskeletal Type Artificial Muscles

This paper proposes a method to increase the inspection speed of in-pipe robots in long, narrow, and complex pipes with long actuator response time including dead time and rise time due to long fluid tubes. The contributions of this paper include describing the novel approach to increase the moving speed of the peristaltic motion type in-pipe inspection robot with an endoskeleton enhancing propulsion and traction. Through the incorporation of a control method considering dead time, the developed peristaltic motion type robot improved its moving speed by 10.0% under standard pressure conditions. In addition, both theoretical and measured data displayed a similar speed enhancement trend. When integrating the high-pressure application method with the control method considering dead time, the resultant moving speed was found to be lower compared to the method without dead time consideration. This discrepancy can be attributed to the slower contraction response of pneumatic actuators relative to pressure response, leading to insufficient contraction during dead time superimposition. Analyzing the contraction response of the pneumatic tube and actuator used in this study revealed that the unit transitioned to the subsequent movement before achieving optimal contraction. However, the control method considering dead time under standard pressure conditions led to a promising 10.0% improvement in moving speed. This result indicates that the robot holds the potential for delivering both great force and speed in its operations. Experimental observations underscored the preference for applying dead time consideration to standard pressure application rather than high-pressure application methods when aiming to enhance performance.
Resource Allocation for Network-Coded Clustered Uplink NOMA Networks with SWIPT-Enabled Relays

This paper considers resource allocation for network-coded clustered uplink non-orthogonal multiple-access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT)-enabled relays. In particular, the goal is to perform joint power allocation, power-splitting, and relay selection (J-PA-PS-RS) to maximize the network sum-rate, subject to quality-of-service requirements. The formulated problem happens to be a non-convex and excessively complex. Thus, it is split into two sub-problems: (1) joint power allocation and power-splitting per relay, and (2) relay selection. Particularly, a solution procedure is proposed, where the joint user and relay power allocation, and power-splitting per relay is solved via a low-complexity iterative three-layer algorithm, and then followed by optimal relay selection. Simulation results have revealed that the proposed solution procedure achieves near-optimal solutions as well as being superior to the OMA-based network.
Cooperative transportation of tether-suspended payload via quadruped robots based on deep reinforcement learning

Transportation of payloads in cooperative manner is common in human life. It involves the policy to suppress payload swing during legged walking, which is quite different from the wheeled transportation. To promote the field of swinging dynamics in multi-robotics system, we discussed the characteristic of two quadruped robots to deal with a tethersuspended payload based on proximal policy optimization reinforcement learning algorithms. Instead of relying on complex model-based control frameworks, the deployment of our method was relatively simple for dynamic walking. The control problem was modeled in simulation and a universal quadruped robot motion control reward mechanism was developed. Domain Randomization was adopt to minimize the Sim2Real gap. Two locomotion methods including omnidirectional walking and directional walking were demonstrated to tracking a rectangle path and an eight pattern path. And the policy was deployed on Go1 quadruped robot in real experiments.
Releasing the Potential of Tensor Core for Unstructured SpMM using Tiled-CSR Format

The GPU has become a popular platform for AI applications, thanks in part to its Tensor Cores that address performance issues. However, the Sparse Matrix Multiplication (SpMM) kernel has remained a bottleneck despite significant advances in computing power. Due to the hardware mechanism of the Tensor Core, its programming granularity does not match SpMM. In this paper, we analyze the reasons why the unstructured SpMM kernel is not suitable for the Tensor Core, and propose the Tiled Compressed Sparse Row (Tiled-CSR) compression format. To address the issue of low non-zero rates in Tiled-CSR format, we exploit the row shuffle algorithm to improve the utilization of Tensor Cores and enhance computing density. We also utilize adaptive memory access modes and 3D-Grid tiling for the SpMM kernel to reduce memory access latency. The experimental results on NVIDIA A100 GPU with matrices in the Deep Learning Matrix Collection (DLMC) demonstrate that the Tiled-CSR format improves the utilization of Tensor Cores under different sparsity, with a maximum of 3.89× at 50% sparsity and a minimum of 1.82× at 90% sparsity compared to the SR-BCRS format. Additionally, our kernel achieves an average speedup of 1.54×(up to 2.12×) over Magicube.
Domain-Specific Machine Learning Based Minimum Operating Voltage Prediction Using On-Chip Monitor Data

Determining the minimum operating voltage ($V_{min}$) of chip designs is critical for low power dissipation and assurance of quality and functional safety during manufacturing tests and in-field monitoring. We demonstrate how on-chip monitor data can be leveraged to provide accurate minimum operating voltage prediction using a domain-specific machine learning approach. Given limited measured chip data, the key challenge in developing a machine learning approach is to provide an accurate prediction while addressing overfitting and selecting a subset of optimal features. To this end, we propose to utilize a novel monotonic lattice neural network architecture that is geared towards accurate prediction by imposing domain-specific monotonic relationships between the input sensor data and $V_{min}$. Furthermore, we perform an effective feature selection by considering both the correlation between each feature and $V_{min}$ as well as the co-linearity between the features. Experiments demonstrate superior performance in comparison with linear regression and conventional neural networks.
Age-of-Information Minimization for Energy Harvesting Sensor in Non-Stationary Environment

In this paper, we consider age-of-information (AoI) minimization for an energy harvesting (EH) source that samples and sends the observations to a sink node. At each time, the source observes the channel quality, the available energy, and the age, and decides whether to sample and send the observation to the sink. We formulate the problem as a Markov decision process (MDP) and establish a threshold structure for the optimal sampling policy. Next, motivated by the popular sliding window upper confidence bound reinforcement learning (SW-UCRL2) algorithm for non-stationary reinforcement learning (RL), we leverage this threshold structure and propose an age-energy-channel based SW-UCRL2 (AEC-SW-UCRL2) algorithm to handle unknown and time-varying energy harvesting rate and channel statistics, when an upper bound to the total variation of each of these quantities over the time horizon is available. Next, for the case when these variation budgets are not available, motivated by the popular bandit-over-RL (BORL) algorithm, we propose an algorithm named age-energy-channel based bandit-over-reinforcement learning (AEC-BORL). Finally, we demonstrate the superior numerical performance of these proposed algorithms via numerical experiments.
Modelling and Compensation for Transmission Error of Timing Belt in Legged Robots

The timing belt transmission offers numerous advantages for legged robots, including high efficiency, impact absorption and large range of joint motion. However, the transmission error under high load remains challenging to locomotion control and further applications of belt transmission. Traditional linear models cannot effectively model the belt deformation under a wide range of tension variations due to the nonlinearity. In this paper, we propose a model of the compensation for the belt transmission error based on the pretension and torque of the pully. The adopted approach bypasses the complexity of elaborate physical model derivations, yielding a non-linear model for transmission system errors through straightforward fitting. Based on the proposed model, an error compensation control is investigated and tested with an one-DoF leg prototype of legged robot. The alignment between experimental results and theoretical analysis demonstrates the accuracy of the modeling and the effectiveness of the error compensation control method. The proposed model provides a convenient and straightforward solution to effectively compensate for the belt transmission errors in legged robots.
A Flexible Towed Aerial Robot System for Stable X-ray Inspection of Power Lines

Regular defect inspection of the power line using X-ray is essential for the maintenance of the power line. Usually, conducting such an inspection with a wheeled robot requires dragging the robot from the ground and carefully placing it upon the power line, which is laborious and unsafe. To improve inspection efficiency, the newly developed unmanned aerial vehicle (UAV) provides a promising alternative. However, the positioning error from Global Navigation Satellite System (GNSS) brings the small-scaled drifting movement of the UAV and X-ray camera system, which leads to imaging blur. To cope with this issue, we design a flexible towed aerial robot system to alleviate the instability of the X-ray camera system. Specifically, the UAV and X-ray camera carrier are flexibly connected by a cluster of ropes, reducing the physical impact from the small-scaled drifting movement of the UAV. The permitting position error tolerance between the UAV and the carrier is analyzed. In addition, a guide wheel frame is designed on the carrier to facilitate the carrier’s smooth rolling along the power line. Furthermore, aiming to adapt to the different types of power lines, we design a lightweight motor-driven system to adjust the camera angles and the imaging plate position. Multi-view cameras are also designed to assist the pilot to control the UAV carrying the X-ray camera system landing on the power line. To verify the performance of the developed aerial robot system, we conduct real-world experiments with double bundle conductors and four bundle conductors. The results show that the developed system can efficiently complete inspection. The X-ray camera could obtain a stable imaging condition under the small drifting movement of the flight.
A Reconfigurable Power Processor for Electric Vehicle Facilitating Both Wired and Wireless Charging

Existing electric vehicles (EV) in the market require two separate power processors for propulsion and on-board wired charging operations. EV with wireless charging feature requires an additional power processing unit to be fitted with the vehicle and thus requires three separate power processors. Existing integrated chargers support any two of these operating modes (i.e., either propulsion and wired charging or wired and wireless charging) out of three required operating modes. In order to optimize size, volume, and weight along with the cost, a novel reconfigurable power processor (RPP) is presented in this article that replaces the need of three separate power processors in an EV. The proposed power processor restructures itself as three different power electronic topologies during three modes of operations. For wireless charging mode, a new converter topology is also proposed in this article for the transmitting unit at the primary side. During both wired and wireless charging methods, the proposed solution draws power from a single-phase ac supply to optimally charge the EV battery pack with CC-CV algorithm and simultaneously maintaining near unity power factor (PF) at the grid side. The proposed idea is validated in the laboratory environment with an experimental set up consisting of a 24 V, 400 W BLDC motor and a 24 V, 30 Ah battery.
Terrain-guided Symmetric Locomotion Generation for Quadrupedal Robots via Reinforcement Learning

Most living organisms in the natural world exhibit inherent symmetry during their locomotion processes. This biological symmetry has inspired this paper to design symmetry reward functions for non-trajectory-based training process of Reinforcement Learning. As a result, the training time to obtain robust policies can be greatly reduced. Besides, this paper also designs a continuously changing terrain randomization routine for the training process, the effect of which indicates that legged locomotion with its intermittent ground contact is the natural choice on uneven rough terrains. The experimental results from stable walking and running behaviors of a quadrupedal robot demonstrate the effectiveness of the obtained policies. The robot can also reject disturbances like human kicks reasonably well during locomotion. Additionally, through extra velocity randomization during the policy training process, a single policy network can be obtained to help the robot track different target velocities actively.
Analysis of Rock-climbing Fish Swimming and Design of a Novel Wire-driven Propeller

The rock-climbing fish "glides" on the underwater wall at a maximum speed of 16 BL/s. Meanwhile, they are also sucker creatures that firmly attach themselves to the underwater wall under the impact of water currents. Previous studies have shown that the integration of suction-climbing functionality in these fish is mainly derived from the microstructure around the suction cups. In this paper, we found that the coordinated deformation of the full body of the rock-climbing fish has a positive effect on the motion of the suction-climbing mode by performing finite element method simulations of the airfoil of the rock-climbing fish in a fluid environment. We then present a novel single-motor-based design of a wire-driven robotic fish for bionic rock-climbing fish swimming, which is capable of realizing different propulsive states of the rock-climbing fish. Our findings provide support for the development of underwater suction-climbing robots based on the bionic rock-climbing fish design, encouraging further research in this area.
Energy Efficient SBS Selection and Resource Allocation for MC-NOMA based HetNets

Heterogeneous networks (HetNets) based on multicarrier non-orthogonal multiple access (MC-NOMA) by allowing several users to share the same spectrum can effectively enhance the system’s spectral efficiency (SE). However, invoking NOMA in HetNets causes the complexity of interference control. In addition, improving energy efficiency (EE) is a critical factor in 5G communication networks. Therefore, in this work, we investigate a joint small base station (SBS) selection, power and sub-channel (SC) allocation problem to maximize system’s EE and manage network’s interference under the constraints of minimum quality of service (QoS) requirement for a downlink MC-NOMA based HetNets. Since the formulated problem is non-convex, we split it into 2 sub-problems. First, we propose an advanced matching-based algorithm for joint SBS selection and SC allocation subproblem. The Dinkelbach and successive convex approximation methods are subsequently employed to address the power allocation sub-problem. The simulation results demonstrate the effectiveness of the considered algorithms.
Deep learning-based techniques for influencing and predicting the impact of public figures' social media statements on the stock market

Stock returns are affected by a variety of factors, among which the social media remarks of public figures are one of the more important aspects on the stock market trend. In this paper, we determine the sentiment type of public figures' social media remarks from the perspective of textual sentiment, and compare them with the stock chart of the day to analyze the connection between the two. Specifically, we first construct a dataset of public figures' social remarks and classify the sentiment types, and then we use the network model BERT for training to be able to judge the sentiment type of a new remark when it is inputted, which serves as a basis for stock prediction. The experiment shows that the public figure's speech will have a strong impact on the stock trading on the same day, but the impact is small for a long time, at the same time, the more influential the public figure is, the more obvious the impact on the stock.
Comparing Human Motion Forecasts in Moving Horizon Trajectory Planning of Collaborative Robots

Shared workspaces between humans and robots are a significant part of Industry 4.0. Modern planning algorithms aim to increase human safety in collaborative scenarios and dynamic environments by considering motion forecasts of obstacles. While highly accurate approaches exist, the question arises whether their often greater complexity justifies the benefits that motion planners can draw from them. The paper at hand compares two motion forecasters and evaluates their effectiveness in a robot motion planner to generate collision-free trajectories online. One motion forecaster uses neural networks for predicting, while the other estimates and extrapolates the state of a customized human skeleton. The evaluation shows that simple and online capable motion extrapolations constitute towards human safety in varying situations more than highly accurate neural network predictions.
Stabilization of a Wind Turbine Tower by Using Multiplicative Controls

This paper aims to address the stabilization of a wind turbine tower model, specifically within the plane of the turbine blades. The model was initially introduced by X. Zhao et al. in [7] and consists of two essential components: a nonuniform NASA Spacecraft Control Laboratory Experiment (SCOLE) system and a two-mass drive-train model incorporating a gearbox. To achieve our objective, we propose two distinct nonlinear feedback mechanisms injected multiplicatively on the boundary. Our approach is constructive and based on energy estimates and the theory of semigroups.
Heart: a Scalable, High-performance ART for Persistent Memory

Concurrent indexes for persistent memory (PM) have been extensively investigated due to the appealing features of PM, such as data persistence and DRAM-comparable performance. Among them, Adaptive Radix Tree (ART) is a widely used index that performs well on variable-sized keys. Nevertheless, existing persistent ARTs still suffer from high PM overhead as well as inefficient concurrency control.In this paper, we present Heart, a persistent ART with low latency and high scalability. Heart proposes a unified node structure for different capacities with Hashed Node and Node Decoupling to reduce the PM access overhead. To achieve high scalability, especially in write-intensive scenarios, we propose an efficient concurrency control protocol with lock-free basic operations and lock-free node split. Furthermore, Heart employs Perceivable Transformation to avoid anomalies during node expansion and shrinkage. Compared to other state-of-the-art persistent ARTs, Heart achieves up to 21.6× higher performance under YCSB workloads with high memory utilization. We also deploy Heart in DRAM and obtain up to 33.4× speedup than the original in-memory ART.
Decomposing Trajectory Forecasting into Route and Motion: Enhancing End-to-end Autonomous Driving with Route Map Inputs

Autonomous driving systems operating in urban environments require precise perception, planning, and accurate control to navigate complex traffic scenarios while respecting traffic rules and ensuring safety. Recent advancements in learning-based end-to-end approaches have showcased remarkable performance in goal-directed navigational scenarios. However, while most state-of-the-art approaches primarily focus on enhancing the perception module for a better understanding of the environment, they adopt a simple GRU-based autoregressive structure for producing waypoints. In this paper, we introduce RM2: Route and Motion prediction with Route Map, a simple yet effective approach that decomposes waypoint prediction into two distinct concepts: route, representing the future path to follow, and motion, determining the trajectory and speed. Through experiments, we discover that this approach is the most effective, leading to superior route completion in closed-loop evaluation. We also demonstrate the benefits of incorporating past route predictions. This way, the RM2 approach significantly outperforms the second-best choice by 50% in the lane-change benchmark routes newly introduced in this work.
Tailoring CUTLASS GEMM using Supervised Learning

General matrix multiplication (GEMM) is a core computation kernel for deep neural networks. CUTLASS, a state-of-the-art open-source CUDA-based linear-algebra template library, provides a highly optimized tiling-based GEMM. However, CUTLASS GEMM often cannot achieve the optimal performance when its tiling configuration is not appropriately chosen because the performance varies significantly depending on some factors such as the tile size and shape, as well as the target graphics processing unit (GPU) architecture. Thus, determining the optimal tiling configuration is a major challenge in achieving the best performance of a tiling-based GEMM.To address this problem, we propose CUTLASS-tailor, a novel end-to-end framework that predicts the best tile parameters for target CUTLASS GEMM operations and underlying GPUs using a neural network model. We trained the prediction model using a suitable synthetic dataset that includes various input matrix combinations with different sizes and structures. Furthermore, to cover the various GPUs with a universal model, we also included the number of GPU cores and the amount of shared memory as GPU hardware features for the input of the CUTLASS-tailor network. On a test dataset from several real-world GEMMs, CUTLASS-tailor-based GEMM operations outperformed the GEMM operations using cuBLAS by up to 1.94× on an NVIDIA TitanXp GPU, and also showed that CUTLASS-tailor can find better tile parameters than well-known search algorithms.
Enhanced Signal Transmission Performance based on Multitap Equalization in Optical FBMC Burst

Due to the impact of predominant inherent imaginary interference (IMI), for the first time in visible light communication (VLC) systems, 3-taps equalizer with flip-filter bank multicarrier (Flip-FBMC) burst is proposed to overcome the degradation performance along with 1-tap equalizer estimation. Over high delays channel profile, the proposed system offers 0.7 dB signal to noise ratio (SNR) gain at $10^{-3}$ of the symbol error rate (SER), while an identical error is obtained at high SNR region corresponding to SER $=10^{-4}$, compared to Flip-OFDM system. Moreover, the possibility to optimize its performance is developed through a tail-shortening mechanism, where $\approx$ 11% increment in its spectral efficiency is obtained when compared to the throughput obtained by Flip-CP-OFDM system. Besides, the theoretical analysis reveals that the induced IMI outside the first order neighbor zone, which cannot be controlled using IAM, has a negligible impact on the estimation accuracy, compared to DCO-FBMC format, where a significant reduction in estimating level is captured.