Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic modeling and Control of a Soft Robotic Arm Using a Piecewise Universal Joint Model

The Piecewise Constant Curvature (PCC) assumption is the most widely used in the modeling and control of soft robots. However, a limitation of PCC models lies in accurately describing the deformation of a soft robot when executing dynamic tasks such as operating under gravity or interacting with humans. This paper introduces a new methodology for dynamic modeling and control of a multi-segment soft arm in the 3D space where each segment undergoes non-constant curvature deformations. In this framework, the soft manipulator is modeled as a series of segments, with each one represented by two stretchable links connected by a universal joint. Furthermore, we devise and analyze a controller for motion control in the configuration space with unknown load. The controller is implemented on a four-segment soft robotic manipulator and validated in a range of dynamic trajectory tracking tasks.
Multi-factor investment strategy based on ChatGPT and deep learning algorithm in new energy automobile industry

As a major emerging industry in the world, the new energy vehicle industry is undergoing rapid development and change. Since its development in 2009, China's new energy automobile industry has accumulated a certain first-mover advantage and scale advantage, and at present, China has the largest number of new energy automobile brands and automobile manufacturers in the world. As the rise of the technology industry and the development of the capital market are closely related, the global capital market's expectation of the development of the new energy automobile industry is also reflected in the securities market. Coupled with the rapid development of Internet information technology, quantitative investment programs based on artificial intelligence have great potential for future development in the financial field. In this paper, we propose a machine learning quantitative trading strategy that takes into account both the sentiment factor and the historical trading data of the financial market. The main role of ChatGPT is to propose and explain different investment strategies by combining the information collected during the market research and analysis phase. This strategy combines two different deep learning models, Natural Language Processing NLP and Time Series Processing LSTM, to extract multidimensional quantitative factors and construct a balanced machine learning based quantitative multi-factor investment strategy for equities. The single-factor strategy is synthesized and compared with the proposed novel multi-factor strategy with equal strategy parameter configurations. The proposed multifactor synthesized strategy is able to achieve higher returns as well as maintain moderate retracement and Sharpe ratios, which outperforms other existing strategies.
Twitching Control for Pneumatic Artificial Muscle Actuators

In this work we present the design and development of a twitching control system for pneumatic artificial muscle (PAM) actuators. In this system, a shape memory alloy (SMA) poppet valve is used to control the flow of air pressurizing the inner bladder of a McKibben-based PAM. The valve is driven by pulse width modulation with changing frequency, allowing the PAM to resemble the mammalian muscles twitching. An adjustable passive exhaust orifice is used in place of an exhaust air valve to release the air pressure from the bladder mimicking natural muscle relaxation behaviour. Experiments under isometric and isotonic contractions show that the proposed control system reduces hysteresis of about 72% compared to continuous activation against 15% loss in contraction force with respect to conventional exhaust air valves.
Chapter 11 Integrating disaster risk reduction and climate change adaptation: Initiatives and challenges in the Philippines

The Philippines is one of the most vulnerable countries in the world as far as climate-related and other forms of disasters (e.g., earthquake and volcanic eruption) are concerned (World Bank and NDCC, 2004; UNISDR, 2008a, 2008b). In 2004 alone, 25 weather disturbances hit the country, of which four occurred successively in November and the first week of December. These triggered massive landslides and flooding in Southern and Central Luzon, leading to damages in lives and properties (Duque, 2005). With this the country is considered as one of the most disaster prone, ranking 12th among the 200 countries most at risk to natural hazards in the 2009 Mortality Risk Index of the United Nations International Strategy for Disaster Reduction (UNISDR, 2009).
Multi-target Approaching Control of Hyper-redundant Manipulators using Reinforcement Learning

Hyper-redundant manipulators have superior dexterity. However, conventional control based on accurate modeling is difficult to achieve. This paper proposes a control method for the hyper-redundant manipulator to complete target approaching tasks using deep reinforcement learning. A hyper-redundant manipulator simulation model was first built and the corresponding forward kinematics equation was analyzed. Then, we used reinforcement learning methods to construct controllers for single target tasks, during which the impact of several key factors on the training was analyzed and optimized accordingly. On this basis, the multi-target task training was achieved by expanding the state to a generalized state. Finally, the proposed control method was verified in a dynamic simulation environment. Simulation results show the effectiveness of the proposed control method.
Blind Walking Balance Control and Disturbance Rejection of the Bipedal Humanoid Robot Xiao-Man via Reinforcement Learning

Bipedal humanoid robot has the ability to both move and manipulate in complex environments, which is of great significance in the future. However, stable bipedal walking in the real world has always been a challenge in industry and even in academia. The traditional model-based methods are highly dependent on the environment, with high modeling complexity and lack of generalization. The solution based on the simplified model usually causes the problem that the control algorithms cannot adapt to complex terrain environment. This paper presents a newly designed bipedal humanoid robot, Xiao-Man. Aiming at achieving the robot’s terrain-adaptive walking behavior, a reinforcement learning based Actor-Critic network with asymmetric structure is proposed. Without using any external perception information, robust bipedal walking behavior of Xiao-Man is achieved. In the process, we also build the dataset based on the joint actuation truth data and train a joint actuator network to reduce the gap between the expected torque and the actual response torque. Experimental results show that the bipedal humanoid robot equipped with the trained control policy achieves the capability of stable walking and disturbance rejection only rely on proprioceptive information.
Research on air quality prediction based on correlation analysis and XGBoost

As one of the primary concerns of people, air quality, its pollution and management is an important topic. Air pollution can cause harm to human health, ecological environment, and social economy, and its pollution level is affected by many factors. Exploring the factors of air pollutant concentration and predicting PM2.5 concentration and AQI index more accurately is a common concern for the scientific community and policymakers. In order to make a more accurate prediction of air quality, this paper addresses the problem of air quality prediction. The quantitative features are extracted based on the Pearson correlation model and stepwise regression model, and then the qualitative features are extracted based on the chi-square test. After the feature extraction, an XGBoost model is constructed and the extracted features are used as input to predict the future air quality. The obtained prediction accuracy is much higher than that of the support vector machine and BP neural network. The predicted R2 is 0.905.
Retracking of SARAL/AltiKa Radar Altimetry Waveforms for Optimal Gravity Field Recovery

ABSTRACT The accuracy of the marine gravity field derived from satellite altimetry depends on dense track spacing as well as high range precision. Here, we investigate the range precision that can be achieved using a new shorter wavelength Ka-band altimeter AltiKa aboard the SARAL spacecraft. We agree with a previous study that found that the range precision given in the SARAL/AltiKa Geophysical Data Records is more precise than that of Ku-band altimeter by a factor of two. Moreover, we show that two-pass retracking can further improve the range precision by a factor of 1.7 with respect to the 40 Hz-retracked data (item of range_40 hz) provided in the Geophysical Data Records. The important conclusion is that a dedicated Ka-band altimeter-mapping mission could substantially improve the global accuracy of the marine gravity field with complete coverage and a track spacing of <6 km achievable in ∼1.3 years. This would reveal thousands of uncharted seamounts on the ocean floor as well as important tectonic features such as microplates and abyssal hill fabric.
The virtual prototype design and simulation of crab walking gaits inspired crawling and barrier-crossing parallel mechanism

In recent years, biomimetic robotics has emerged as a promising field that draws inspiration from nature to develop innovative and efficient robots. In this study, we have designed a biorobot capable of crawling and barrier-crossing, inspired by the walking gaits of crabs. Firstly, we analyzed the motion of an individual crab leg to determine the required degrees of freedom for crawling and barrier-crossing. Then, we utilize the screw theory to synthesize the mechanism of a single branch. Finally, we select a suitable parallel mechanism configuration for this research. Simulation analysis is conducted to test the variations in actuation at different driving points under various pose states. We observe stability in changes in driving points with respect to the retraction angle of the leg, indicating excellent obstacle overcoming capabilities possessed by this robot.
AiMap: Learning to Improve Technology Mapping for ASICs via Delay Prediction

Technology mapping is an essential process in the EDA flow which aims to find an optimal implementation of a logic network from a technology library. In ASIC designs, the estimated cell delay w.r. t. the cut has a significant impact on both area and delay of the mapped network. In this work, we first propose formulating cell delay estimation as a regression learning task by incorporating multiple perspective features, such as the structure of logic networks and non-linear cell delays, to guide the mapper search. We design a learning model that incorporates a customized attention mechanism to be aware of the pin delay and jointly learns the hierarchy between the logic network and library, with the help of proposed parameterizable strategies to generate learning labels. Experimental results show that our proposed method noticeably improves area by 12% and delay by 1%, compared with ABC.
Hidden Permutations to the Rescue: Multi-Pass Streaming Lower Bounds for Approximate Matchings

We prove that any semi-streaming algorithm for $(1+\varepsilon)$ approximation of maximum bipartite matching requires \begin{equation*}\Omega\left(\frac{\log (1 / \varepsilon)}{\log (1 / \beta)}\right)\end{equation*} passes, where $\beta \in(0,1)$ is the largest parameter so that an n-vertex graph with $n^{\beta}$ edge-disjoint induced matchings of size $\Theta(n)$ exist (such graphs are referred to as Ruzsa-Szemerédi graphs). Currently, it is known that \begin{equation*}\Omega\left(\frac{1}{\log \log n}\right) \leqslant \beta \leqslant 1-\Theta\left(\frac{\log ^{*} n}{\log n}\right)\end{equation*} and closing this huge gap between upper and lower bounds has remained a notoriously difficult problem in combinatorics.Under the plausible hypothesis that $\beta=\Omega(1)$, our lower bound result provides the first pass-approximation lower bound for (small) constant approximation of matchings in the semi-streaming model, a longstanding open question in the graph streaming literature.Our techniques are based on analyzing communication protocols for compressing (hidden) permutations. Prior work in this context relied on reducing such problems to Boolean domain and analyzing them via tools like XOR Lemmas and Fourier analysis on Boolean hypercube. In contrast, our main technical contribution is a hardness amplification result for permutations through concatenation in place of prior XOR Lemmas. This result is proven by analyzing permutations directly via simple tools from group representation theory combined with detailed information-theoretic arguments, and can be of independent interest.
Toward Green Access Management for IoT Environments Using eBPF

With IoT (Internet of Things) becoming more common and widely used, users’ data is getting more exposed to different networks. One of the challenges raised by this situation is to keep the data safe while delivering it to the intended consumers over the network without any security issues. Using firewalls to control access to the data within or across the network can be a reasonable solution. However, traditional firewalls require a large amount of processing power; thus, they consume a lot of energy. A significant portion of the power is used to copy the packets between different environments and allocate various resources to the packets. In this paper, we use eBPF to create a firewall that processes packets as low as possible in the network stack. This eliminates the need to copy packets multiple times for dropped or redirected packets. We also provide comparisons between traditional firewalls like iptables and an eBPF-based firewall in terms of consumed energy and time. We also discuss why eBPF-based firewalls are more suitable when it comes to IoT.
A Real-time Construction of Raster Maps Based on Improved Data Normalization

Aiming at the current ORB-SLAM2 error problems in feature extraction and point cloud segmentation in the real-time acquisition of point cloud data scenarios, a real-time construction algorithm of raster maps based on data generalization is proposed to adapt to the real-time navigation and obstacle avoidance of the intelligent force and other task requirements. The algorithm considers the integrity of the environment configuration before verifying the real-time collection of point cloud data to guarantee the effect of real point cloud data, and then extracts the coordinate information of the point cloud data and improves the data generalization of the extracted point cloud data after retaining the three-dimensional coordinate information; then realizes the real-time construction of raster maps based on the point cloud data segmentation algorithm. The TUM open source dataset and the measured data are used for experimental verification to ensure the feasibility of the algorithm, and the real-time experiments are conducted with a monocular depth camera to realize the real-time construction of raster maps, which verifies the effectiveness of the proposed algorithm.
The Chief Financial Officer's Characteristics and the Impacts on Driving Organization's Future: The Upper Echelons Theory

The purpose of this study is to investigate the relationship between the characteristics of Chief Financial Officer (CFO) and their roles in driving an organization's future. This study is based on the Upper Echelons Theory, which states that the characteristics of management influence organizational outcomes. A survey was conducted with 81 CFOs from publicly traded companies, private domestic firms, and foreign multinational corporations from various industries in Thailand. The study investigated how CFO characteristics such as years of experience and educational background influenced defined CFO roles. Our study found no significant impact of CFO educational background on their roles. Years of experience in the CFO position had a significant impact on roles such as managing risk, delivering business performance, assessing investment efficiency, leading digital transformation, and maintaining integrity and ethics roles. This study advances the ability to predict organizational outcomes beyond existing theories. By taking into account their CFO characteristics, our findings can be used to forecast targeted companies or competitors on a move with a projection. The findings of our research are relevant and broadly applicable to an international audience, owing to the mixed nationality of CFO respondents and the variety of company origins.
KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications

Nowadays, Graphics Processing Units (GPUs) dominate in a wide spectrum of computing realms and multi-task is increasingly applied in various complicated applications. To gain higher performance, multi-task programs require cumbersome programming efforts to take advantage of inter-kernel concurrency at source-code level. Although there exist works automatically scheduling kernels to enable inter-kernel concurrency, they all inevitably introduce new programming frameworks and some even bring significant performance downgrade compared to the expertise-based optimizations. To address this issue, we propose KeSCo, a compiler-based scheduler to expose kernel level concurrency in multi-task programs with trivial code modification. In compilation, KeSCo applies a strategy to schedule kernels in task queues, accounting for both load balance and synchronization cost. Also, KeSCo utilizes a customized algorithm designed for computational flow to remove redundant synchronizations. The design is further extended to support multi-process scenario, where multiple GPU processes are sharing a single context. Evaluations on representative benchmarks show that the proposed approach gains a 1.28× average speedup for multi-task scenario (1.22× for multi-process). Even with lessened programming efforts, our proposed design outperforms two state-of-the-arts GrSched and Taskflow by 1.31× and 1.16× on average, respectively.
Design and MLP-based Kinematics Modeling of a Gastrointestinal Endoscopy Robot

The operation of traditional endoscopes for gastrointestinal endoscopy (GE) is challenging, which facilitates research into autonomous robotic endoscopes. However, these robots face safety and clinical adaptability. Meanwhile, precise motion control of the endoscope’s bending section is essential for autonomous GE. Due to its driving characteristics, the bending section exhibits nonlinearity, particularly in terms of backlash, making precise and efficient analytical modeling difficult. Therefore, we first present a robot system, which is based on the traditional endoscope, to robotize the traditional endoscope. Then, we introduce a Multi-Layer Perceptron(MLP)-based kinematic modeling method with backlash compensation to enhance open-loop accuracy. Experimental results demonstrate that the proposed method effectively reduces system nonlinearity, improves learning model accuracy, and significantly outperforms traditional analytical and learning models in open-loop path tracking precision based on inverse kinematics.
A General Huber Function Based Compliant Control Method for the Cooperative Puncture Surgery

The positional smoothness and stability of the medical instrument is crucial in a surgery. However, the instrument is subject to disturbance from human muscle biological tremor. In case of puncture surgery, the needle insertion with positional jitters decreases the safety and curative effect of the surgery. In this study, we introduce the general expression of the Huber function which can suppress positional disturbance with low amplitude. Then, we combine the function with the commonly used impedance model to form a compliant control method. This method takes advantages of the Huber function in restraining disturbance and the impedance model in smoothing the robotic motion. Then, we construct a prototype of a puncture system to verify the effectiveness of the proposed control method. Comparative experiments manifest the capacity and feasibility this method.
Investigation of bio-inspired tail-first swimming using numerical and robotic models

In contrast to head-first swimming of most natural swimmers such as fishes, the larvae of mosquito swim tail-first with the posterior end of the body leading the anterior end in the direction of locomotion. In order to gain comprehensive insights into the mechanics of tail-first swimming, the gait kinematics was quantified through markerless tracking, and flowfield was computed using Particle Image Velocimetry (PIV) analysis, revealing that the generation of peak hydrodynamic forces was at mid-stroke. Parameterization of the kinematics facilitated self-propelled numerical simulations using immersed boundary lattice Boltzmann method (IB-LBM) to be performed and the implementation of kinematics on a tethered robotic model. The parameterization produced tail-first swimming in the numerical simulations and the dye flow visualization from robotic experiments reveals the direction of momentum transfer facilitates tail-first locomotion. Furthermore, force measurements on the robotic model provide quantitative evidence supporting tail-first swimming resulting from this gait
Compaction of Functional Broadside Tests for Path Delay Faults Using Clusters of Propagation Lines

Functional broadside tests detect delay faults that can affect the correct functional operation of the circuit without overtesting. When path selection is performed to address the number of path delay faults it is important if among the target faults there are ones that can affect the correct functional operation of the circuit. A large pool of functional broadside tests is obtained when the tests are extracted from available functional sequences. In this case, tests that are extracted later may be effective for test compaction even if they do not detect new faults. This article addresses the problem of test compaction for path delay faults in this scenario. The main contribution of the article is related to the use of clusters of lines satisfying propagation conditions for path delay faults. When a test results in a cluster that is not contained in a cluster obtained for any test considered earlier, the test is potentially important for test compaction. Tests whose clusters are contained in those of other tests are discarded. Experimental results for benchmark circuits demonstrate the importance of considering tests that do not detect new path delay faults, and the effectiveness of clusters.