Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of compliance error compensation approaches for robotic manipulators with double encoders

The paper deals with the comparison analysis of build-in and model-based control strategies for robots with double encoders. Particular attention is paid to the efficiency evaluation of the related compliance error compensation techniques. Three different model-based control strategies were examined. It was shown that reduced stiffness model-based and joint-compliance-ratio-based strategies could compensate twice as much as built-in feedback control based on the secondary encoder and reduce 77% of positioning errors. The complete stiffness model achieved a 97% compliance error compensation level and regardless of its complexity, it is a reasonable approach for the robots involved in high-accuracy contact applications.
Digital Twin System for Robotic Tool-replacing in Tunnel Boring Machine

Tunnel boring machine (TBM) tool-replacing robot play an important role in improving the efficiency and safety of tunnel construction. However, considering the complexity and specificity of the working environment, current robotic tool-replacing solutions still have deficiencies, including the lack of customized motion simulation tools, the difficulty of human-robot interaction due to confined space, and the lack of management of robot operation data. To address these issues, this paper designs a digital twin system for robotic tool-replacing in TBM. The system builds a virtual working environment using Unity3D, and establishes a kinematic model of the robot to achieve the pre-planning of the movement trajectory. The synchronous mapping between the digital model of the robot and the physical entity is achieved through Socket communication technology, thus realizing the digital monitoring of the tool-replacing process. In addition, twin databases of the system are built based on SQLite for storing and managing the robot's operation data. Finally, the effectiveness of each functional module of the system is proved through experiments.
Mimicking DH Key Exchange Over a Full Duplex Wireless Channel via Bisparse Blind Deconvolution

Secret key generation between authenticated devices is a pivotal task for secure communications. Diffie-Hellman (DH) is de-facto standard but not post-quantum secure. In this paper, we shall invent and analyze a new security primitive that is specifically designed for WPAN. For WPAN, wireless channel-based secret key generation has been proposed but was not widely deployed due to its critical dependence on the channel’s entropy which is uncontrollable. We formulate a different approach: We still exploit channel properties but mainly hinge on the reciprocity of the wireless channel and not on the channel’s entropy. The radio advantage comes from the use of full duplex communication. We show that in this situation both legitimate parties can agree on a common secret key even without ever probing the channel. At the core is a new bisparse blind deconvolution scheme for which we prove correctness and information-theoretic, i.e. perfect, security. We show that, ultimately, a secret key can be extracted and give a lower bound for the number of secret key bits which is then verified by experiments. We also notice a remote correspondence of the scheme to DH key exchange.
A Trajectory Planning Framework for Experimental Module Manipulator On-Orbit Servicing

This article proposes a novel trajectory planning framework for the Experimental Module Manipulator (EMM) on-orbit servicing (OOS). For a start, the simulation system of the EMM based on Open Inventor is developed, and the trajectory planning technical route of the EMM for OOS missions is illustrated, including two main phases: large-scale transfer and small-scale approach. Before trajectory planning, the Gilbert–Johnson–Keerthi convex bounding boxes of the CSS and the EMM are modeled. Moreover, we present a Cartesian-space collision-free planning scheme. For the different task requirements in the above two phases, two corresponding collision-free trajectory planning schemes in joint space are proposed. Finally, simulations of the EMM supporting an astronaut extra-vehicular activity verify the effectiveness of our trajectory planning framework and all of the schemes presented in this article.
Music depth recommendation algorithm based on user data mining

Mass information will generate too many choices while providing services, making it difficult for users to make choices. How to analyze user needs based on users' historical listening records to achieve song recommendation is one of the challenging topics in the field of music recommendation. Existing music recommendation methods simply use all the music that the user has listened to as the context of music recommendation, resulting in the same context weights learned for different types of music, which seriously affects the accuracy of music recommendation. In order to improve the accuracy and personalization of music classification, this paper proposes the use of a deep music recommendation algorithm based on user data mining technology in music classification. Tendency, construct behavior feature space, and combine factor decomposition machine theory to predict user behavior types, as the basis for personalized music recommendation.
Towards an Ontological Framework for Validity Frames

A Validity Frame captures the set of contexts in which a model (and its analysis, often by means of simulation) of a system is able to replace that system with respect to questions about a set of salient properties of interest. Even though the utility of validity frames has been reported in current literature, there does not exist any precise and general definition of the concept. This paper presents our on-going development of a framework for designing and using validity frames. This framework both uses and supports model management. We have developed an ontology in order to precisely define the concepts of the model validity domain. The framework currently consists of ontological definitions integrated in a workflow model that describes a general experiment, validation experiments, and the construction of validity frames. A simple resistor model validation case-study is used as running example to describe the concepts. The validity frames of different resistor models are computed. How to use the framework in different scenarios is sketched.
Gotten: A Model-Driven Solution to Engineer Domain-specific Metamorphic Testing Environments

Testing is essential for assessing the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, creating an MT environment for a specific domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort. To alleviate these difficulties, we present a model-driven tool that automates the construction of MT environments. Starting from a meta-model with the domain concepts, and a description of the domain execution environment, our tool produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases. This paper illustrates the tool on a case-study in the domain of video streaming APIs. A video showcasing the tool is available at https://youtu.be/DeuIW6V4LaQ.
Design and Modeling of A Lightweight Lower Limb Exoskeleton With Compliant Knee Joints

Lower limb exoskeletons for gait rehabilitation have been widely studied, including rigid and flexible exoskeletons. In order to improve the human-robot coordination during exoskeleton wearing, avoid the constraints on joints caused by rigid structures, this paper designed a lightweight lower limb exoskeleton with compliant knee joints. By adopting a lightweight design, the main body is composed of carbon fiber and aluminum alloy, resulting in a total weight of only 7KG. Different from the current knee joint cable-driven structure that install the joint motor on the back, we install the knee joint motor on the thigh and use two completely parallel Bowden cables to transmit force. This structure can reduce the mass and rotational inertia at the knee joint, improve the wearable characteristics of the exoskeleton, and reduce the additional frictional losses caused by long-distance force transmission of the Bowden cables. Meanwhile, the exoskeleton adopts a modular design, which facilitates its application in different assistive modes. Finally, through the joint encoders and motor PD control to track the joint trajectory, the measurement results show that the error for the hip and knee joints is within 2° and 3.5° respectively.
Path Planning for Robotic Arm Based on Reinforcement Learning under the Train

Due to the widespread use of robotic arms, path planning for them has always been a hot research topic. However, traditional path planning algorithms struggle to ensure low disparity in each path, making them unsuitable for operation scenarios with high safety requirements, such as the undercarriage environment of train. A Reinforcement Learning (RL) framework is proposed in this article to address this challenge. The Proximal Policy Optimization (PPO) algorithm has been enhanced, resulting in a variant referred to as Randomized PPO (RPPO), which demonstrates slightly accelerated convergence speed. Additionally, a reward model is proposed to assist the agent in escaping local optima. For modeling application environment, lidar is employed for obtaining obstacle point cloud information, which is then transformed into an octree grid map for maneuvering the robotic arm to avoid obstacles. According to the experimental results, the paths planned by our system are superior to those of RRT* in terms of both average length and standard deviation, and RPPO exhibits better convergence speed and path standard deviation compared to PPO.
Frequency Stability Analysis of Non-Conventional Renewable Integrated Power Systems

Integrating intermittent non-conventional renewable energy sources such as wind and solar into power systems presents unique challenges for its operation. To address this, effective frequency control mechanisms are crucial. This study presents a comprehensive analysis investigating the performance of hydro and steam, in a non-conventional renewable integrated power system. The analysis aimed to enhance power system stability through the collective operation of wind and solar power plants of varying capacities, while maintaining a stable frequency. In addition, the study investigated the maximum penetration level of these resources under different contingencies. Preliminary results indicate that the hydro turbine exhibits a longer settling time and higher steady-state error compared to its steam turbine counterpart. Using a 12 bus test system simulated in PSCAD software, this research provides valuable insights into turbine performance and the feasibility of integrating renewable resources, aiding in the improvement of power system stability.
Secure Computation Meets Distributed Universal Optimality

We present a new algorithmic approach to distributed secure algorithms that is based on combining two independent lines of research: secure computation and distributed universal optimality. Our end result provides round-efficient distributed algorithms that protect the privacy of the graph vertices against a (possibly large) coalition of semi-honest adversaries.Secure Computation: The notion of perfect privacy dates back to Yao [FOCS ’82], and has been extensively addressed by the Cryptographic community over the years. Most of the prior work considers the Multi-Party-Communication (MPC) model, in which the parties are fully connected. Considerably less is known on the (round) complexity of secure algorithms for general graphs, especially under the classical message passing models, such as the CONGEST model. For any biconnected D-diameter graph, a recent line of works [Parter and Yogev, SODA 19, ICALP 19, PODC 19] presented a simulation result that protects against a single semi-honest corruption, by paying an overhead of $D \cdot \mathsf{poly}(\Delta)$ CONGEST rounds, where $\Delta$ is the maximum degree. Due to an inherent structural barrier, the generalization of the current framework to handling f corruptions, provably leads to an overhead of $O(\Delta D)^{\Theta(f)}$ rounds. This can also be shown to be tight for the class of store-and-forward algorithms1.1In which nodes can only propagate messages as atomic units, without the ability to mix multiple messages together.Secure Computation & Universal Optimality. We present an improved framework for secure computation which bypasses the current exponential in f barrier. For every graph $G=(V, E)$ with vertex-connectivity $\widetilde{\Omega}(f)$, our simulation provides a round overhead of $\mathsf{poly}(\Delta) \cdot \widehat{O}(\mathbf{S Q}(G))$. 2 The graph measure $\mathbf{S Q}(G)$ (Shortcut Quality) captures the universal optimal complexity of many network optimization tasks in the (non-secure) CONGEST model, as demonstrated in a recent breakthrough result of [Haeupler, Wajc and Zuzic, STOC 2021]. We are hopeful that the extended graph-theoretic machinery provided in this paper will find further applications in secure computation and beyond.2The notation $\widehat{O}(\cdot)$ hides $2^{O(\sqrt{\log n})}$ factors.
K8sES: Optimizing Kubernetes with Enhanced Storage Service-Level Objectives

Kubernetes (k8s) is a system for managing containerized applications across multiple hosts. It offers automatic deployment, maintenance, scaling, and resource management for applications. Applications in k8s usually have different storage requirements in the form of service-level objectives (SLOs). However, the current k8s storage management has several limitations which cause explicit performance and cost overhead. K8s administrators have to configure storage in advance manually, and users must know configurations and capabilities of provided storage. Users' storage SLOs can be easily violated in k8s.In this paper, we design and implement k8s Enhanced Storage (k8sES) which efficiently supports applications with various storage SLOs along with all other requirements in the Kubernetes environment. We design and incorporate storage scheduling as part of the node scheduling process in k8s. Applications will be scheduled onto the correct nodes and storage without intervention from either users or administrators. Proper storage resources will be dynamically carved based on users' storage SLOs. In addition, we provide a tool to monitor the I/O activities of both applications and storage devices in k8sES. The evaluation shows that k8sES can better meet users' storage SLOs along with other requirements. Also, k8sES can achieve higher resource utilization efficiency with overhead similar to that of the current k8s.
SWhegPro3: A Three-Impeller Wheel-Leg Transformable Robot with Variable Robust Adaptability to Stair Dimensions

This paper reports the design innovation and implementation of a novel wheel-leg transformable robot named SWhegPro3. The robot uses a three-impeller design to significantly improve the overall stair-climbing performance. Unlike other impeller-based designs that use gear sets to unfold the impellers, this robot uses self-locking electric push rods for mode switching, thereby greatly improving its load capacity. We also analyze the stair climbing operation and derive a model to calculate the optimal configuration of the wheel-leg module and the corresponding displacement of the push rod, in order to better adapt to different sized stairs. We conduct both simulation and field experiments to validate our proposed design and control strategy.
Hybrid6D: A Dual-Stream Transformer-CNN Approach for 6D Object Pose Estimation from RGB-D Images *

Estimating the 6D pose presents a formidable challenge due to the intricate nature of world objects and the myriad of issues encountered when acquiring data from real-world scenes. This task is compounded by occlusions, fluctuating lighting conditions, and data noise. The conventional CNN or Transformer architectures primarily focus on either local or global information, and relying solely on RGB or depth maps proves inadequate for extracting the most robust features. To address these challenges, we introduce Hybird6D, a dual-stream fusion deep learning approach that leverages a Transformer-CNN structure and attention mechanism to regress the 6D pose of objects using RGB-D images as input. Our methodology capitalizes on the strengths of both Transformers and CNNs, incorporating global attention and local attention, while simultaneously harnessing both appearance and geometric information. This comprehensive approach enhances robustness to noise and occlusions. Experimental evaluation on the YCB-Video dataset demonstrates that our method outperforms existing Transformer-based approaches and surpasses advanced CNN-based methods. Moreover, our approach exhibits superior performance on symmetry objects, textureless objects, and small objects.
Encountered-Type Tabletop Haptic Display for Objects On-Demand in Virtual Environments

This paper presents the design and capabilities of a custom-built Encountered-Type Haptic Display. With an X-Y-Z-Yaw plotter mechanism below the tabletop and four permanent magnets in the end-effector, the device can manipulate multiple objects in three dimensions on top of the table. Four hall effect sensors in the end-effector are used to compensate the friction of the display and increase the positioning accuracy of objects to 0.5 mm. With an end-effector speed of 66.6 cm/s, objects can be placed in a workspace of 47.2×26 cm. The device is built for human-computer interaction scenarios in virtual reality (VR) and augmented reality (AR) to provide haptic feedback on-demand. Due to the design, arbitrary object shapes and materials can be presented to the user within the table’s workspace. The usability and performance of the table are evaluated with a user study.
Terrain Classification Using Inside-Wheel Cameras Based on Wheel-Terrain Interaction Characteristics

The wheeled rover is widely used in off-road planetary exploration tasks nowadays. However, complex operating environment like soft deformable terrains on the planet surface demands the high performance of rover. In order to reduce danger, environmental perception technologies need to be used on the rover. Terrain classification is a critical method in environmental perception. In this research, a wheel-vision-based terrain classification method is proposed. This method uses wheel-terrain interaction parameters obtained from inside-wheel cameras which are strongly related to the terrain as input. We use single-wheel testbed to collect and process data to form a dataset. The data is obtained under different slip ratios and different terrains. Then, we train four terrain classification models using machine learning algorithms and analyze their performance. From the results, all performance criteria of the models achieve over 95%, with DT being the optimal method. The feasibility of our method in the case of fewer training samples and the rationality of dataset selection are also verified through the results of different training set sizes and the ablation experiment.
A New Architecture For Diagnosing Pulmonary Thorax Diseases (Covid-19, Pneumonology, Normal) Using Deep Learning Technology

Medical imaging is an efficient field of research because it makes it possible to diagnose and detect diseases and relieve patients by presenting a remedy or treatment to follow. Pulmonary pathology represents a principal challenge for doctors to diagnose and examine the type of disease. The use of medical x-ray images has made it possible to advance research in terms of medical findings by specialists. Despite this, doctors sometimes misdiagnose the patient, and we suggest employing artificial intelligence to significantly enhance the patient’s medical diagnosis and help identify lung ailments more accurately. In this project, we plan to develop a computer system based on deep learning with a coherent approach based on the application of standard convolutional neural networks (CNN) with architectures appropriate and specific to this problem (VGG16, MobileNetV1, ResNet50, InceptionV3, DenseNet121, and DenseNet169). We also propose the construction of an architectural model called RafikNet. The tests will be realized on a corpus of images with three types of lung disease (COVID-19, pneumology, and normals), and we will evaluate our approach on parameters such as accuracy, precision, recall, f1-score, support, and loss.
Non-Invasive Tools for Early Detection and Monitoring of Sarcopenia in Older Individuals

Sarcopenia, defined by a gradual loss of muscle mass and function, affects many elderly people and is associated with reduced mobility, greater frailty, and a higher risk of falling. Using a vision attentive model and integrating the embedded Timed Up and Go Test (TUG-T), 3-meter Walk Test $(3\mathrm{~mW}-\mathbf{T})$, and fall risk analysis, this research proposes a unique method for assessing sarcopenia in elderly people. The attentive vision model uses computer vision techniques to examine TUG activities and gait speed in real-time, offering insightful information about the elderly’s the functional ability and muscular strength. Moreover, this approach provides a more comprehensive assessment of sarcopenia by integrating falling risk analysis. The proposed system achieved an overall accuracy of $ 86.6\%$, outperforming the individual components: TUG test $(84.0\%,p\lt0.05)$, gait speed $(88.2\%,p\lt0.05)$, and fallen risk assessment $(93.0\%,p\lt0.05)$. The results indicate that this novel strategy has enormous potential for aged healthcare, enabling targeted therapies and enhancing the overall quality of life for older people at risk of issues connected to sarcopenia.
A Domain-Driven Model Generation Framework for Cyber-Physical Production Systems

The growing influence of Information Technologies in the manufacturing domain has led to the fourth industrial revolution (Industry 4.0). Cyber-Physical Production System (CPPS) is one of the fundamental concepts of Industry 4.0 that aims to develop an intelligent manufacturing environment by leveraging concepts like the Internet of Things (IoT), cloud computing, virtualization, and Artificial Intelligence (AI). However, the challenges originating from the technological heterogeneity in the manufacturing domain remain primary obstacles towards realising a fully automated CPPS. Among them, semantic heterogeneity in manufacturing information is the most crucial which can be attributed to technology and vendor-specific information modelling mechanisms. A CPPS requires seamless machine-to-machine communication which could be hindered due to the non-interoperability among machine data on a semantic level. Therefore, the primary focus of this thesis work is to understand the semantic interoperability challenges of CPPS and propose solutions to address those challenges. The proposed solution revolves around the development of semantic domain models using the modelling philosophies of Domain-Driven Design (DDD).
Learning to traverse challenging terrain using BGRU-Attention*

In this paper, we present a model-free deep reinforcement learning (DRL) algorithm to control the locomotion of a quadruped robot adaptively at challenging terrain. This approach is used to improve the robot’s ability to traverse unstructured ground. Combining Bidirectional Gate Recurrent Unit (BGRU) and attention mechanism, we train the quadruped robot to climb up and down stairs, traverse various field terrain and bricks. We train the controller in simulation using knowledge distillation, and deploy on real robots without any adjustment. In the simulation, the performance of our Student Module even exceeds that of Teacher Module. The experimental results demonstrate that our proposed approach can make the quadruped robot traverse obstacles swiftly.