検査や操作など多様なタスク遂行のためのクワッドローター飛行口ボットのモデリングと非線形制御

京都大学新制・課程博士博士(工学)甲第23504号工博第4916号新制||工||1768(附属図書館)京都大学大学院工学研究科機械理工学専攻(主査)教授 松野 文俊, 教授 泉田 啓, 教授 藤本 健治学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDGA

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

Modeling and Control of a Quadrotor UAV Equipped With a Flexible Arm in Vertical Plane

In the field of unmanned aerial vehicles (UAVs), aerial manipulations are receiving considerable attention because of their potential application to tasks such as pick and place, detection, and inspection. However, short flight endurance times and concerns about the safety to surroundings during interacting heavily limit the expansion of aerial manipulations in real implementations. To overcome these challenges, this paper focuses on a system in which a quadrotor UAV is equipped with a lightweight and flexible arm. Based on the infinite-dimensional dynamics, the mathematic model of system is described by a hybrid partial differential equation-ordinary differential equation (PDE-ODE). An easily implementable controller is derived from a Lyapunov functional construction related to the energy of the system. The proposed controller ensures global Lyapunov stability for nonlinear system and local asymptotic stability for the linearized system. Further, it is shown that the proposed controller realizes stable motion of the aerial manipulator as well as vibration control of the flexible arm. Finally, numerical simulations are conducted to investigate the validity of the proposed controller

Analysis on cushion performance of quartz sand in high-g shock

Abstract The cushion protection for light mass electronic instruments in projectile is of vital importance to the normal work of an ammunition system. Quasi-static compression tests were conducted on two kinds of quartz sand with different grain diameters and their energy absorption abilities were analyzed. The cushion effect under high g shock was studied by using air gun. The results of experiments show that the quartz sand material takes in energy by grain breakage and the energy absorption ability in unit volume, the energy absorption ability in unit mass and the ideal energy absorption efficiency all improve with the increase of grain diameter. The cushion efficiency of the coarse quartz sand material with grain diameter of 1.0mm to 5.0mm can reach more than 50% under high g shock. This provides a favorable cushion protection for light mass equipment

Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning

Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multi-modal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks

Thrombocytopenia Is Associated with Acute Respiratory Distress Syndrome Mortality: An International Study

Background: Early detection of the Acute Respiratory Distress Syndrome (ARDS) has the potential to improvethe prognosis of critically ill patients admitted to the intensive care unit (ICU). However, no reliable biomarkers are currently available for accurate early detection of ARDS in patients with predisposing conditions. Objectives: This study examined risk factors and biomarkers for ARDS development and mortality in two prospective cohort studies. Methods: We examined clinical risk factors for ARDS in a cohort of 178 patients in Beijing, China who were admitted to the ICU and were at high risk for ARDS. Identified biomarkers were then replicated in a second cohort of1,878 patients in Boston, USA. Results: Of 178 patients recruited from participating hospitals in Beijing, 75 developed ARDS. After multivariate adjustment, sepsis (odds ratio [OR]:5.58, 95% CI: 1.70–18.3), pulmonary injury (OR: 3.22; 95% CI: 1.60–6.47), and thrombocytopenia, defined as platelet count <80×103/µL, (OR: 2.67; 95% CI: 1.27–5.62)were significantly associated with increased risk of developing ARDS. Thrombocytopenia was also associated with increased mortality in patients who developed ARDS (adjusted hazard ratio [AHR]: 1.38, 95% CI: 1.07–1.57) but not in those who did not develop ARDS(AHR: 1.25, 95% CI: 0.96–1.62). The presence of both thrombocytopenia and ARDS substantially increased 60-daymortality. Sensitivity analyses showed that a platelet count of <100×103/µLin combination with ARDS provide the highest prognostic value for mortality. These associations were replicated in the cohort of US patients. Conclusions: This study of ICU patients in both China and US showed that thrombocytopenia is associated with an increased risk of ARDS and platelet count in combination with ARDS had a high predictive value for patient mortality

Effects of Hydrogen Peroxide on Wound Healing in Mice in Relation to Oxidative Damage

10.1371/journal.pone.0049215PLoS ONE711

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

As a new education product in the information age, Massive Open Online Courses (MOOCs) command momentous public attention for their unexpected rise and flexible application. However, the striking contrast between the high rate of registration and the low rate of completion has put their development into a bottleneck. In this paper, we present a semantic analysis model (SMA) to track the emotional tendencies of learners in order to analyze the acceptance of the courses based on big data from homework completion, comments, forums and other real-time update information on the MOOC platforms. Through emotional quantification and machine learning calculations, graduation probability can be predicted for different stages of learning in real time. Especially for learners with emotional tendencies, customized instruction could be made in order to improve completion and graduation rates. Furthermore, we classified the learners into four categories according to course participation time series and emotional states. In the experiments, we made a comprehensive evaluation of the students&rsquo; overall learning status by kinds of learners and emotional tendencies. Our proposed method can effectively recognize learners&rsquo; emotional tendencies by semantic analysis, providing an effective solution for MOOC personalized teaching, which can help achieve education for sustainable development

Random pruning: channel sparsity by expectation scaling factor

Pruning is an efficient method for deep neural network model compression and acceleration. However, existing pruning strategies, both at the filter level and at the channel level, often introduce a large amount of computation and adopt complex methods for finding sub-networks. It is found that there is a linear relationship between the sum of matrix elements of the channels in convolutional neural networks (CNNs) and the expectation scaling ratio of the image pixel distribution, which is reflects the relationship between the expectation change of the pixel distribution between the feature mapping and the input data. This implies that channels with similar expectation scaling factors ( $\delta _{E}$δE ) cause similar expectation changes to the input data, thus producing redundant feature mappings. Thus, this article proposes a new structured pruning method called EXP. In the proposed method, the channels with similar $\delta _{E}$δE are randomly removed in each convolutional layer, and thus the whole network achieves random sparsity to obtain non-redundant and non-unique sub-networks. Experiments on pruning various networks show that EXP can achieve a significant reduction of FLOPs. For example, on the CIFAR-10 dataset, EXP reduces the FLOPs of the ResNet-56 model by 71.9% with a 0.23% loss in Top-1 accuracy. On ILSVRC-2012, it reduces the FLOPs of the ResNet-50 model by 60.0% with a 1.13% loss of Top-1 accuracy. Our code is available at: https://github.com/EXP-Pruning/EXP_Pruning and DOI: 10.5281/zenodo.8141065

Stability and in vitro digestibility of beta‐carotene in nanoemulsions fabricated with different carrier oils

Beta‐carotene, the main dietary source of provitamin A, is required for maintaining optimum human health. The bioaccessibility of beta‐carotene can be greatly improved when ingested with fat. Therefore, the aim of the current study was to select proper oils (palm oil, coconut oil, fish oil, and corn oil) as a carrier to form stable nanoemulsion that can effectively enhance the bioaccessibility of beta‐carotene. The nanoemulsion was formulated with 90% (v/v) aqueous solution (2% whey protein isolate, WPI, w/v) and 10% (v/v) dispersed oil. The in vitro digestion experiment of nanoemulsions showed that the bioaccessibility of beta‐carotene was as followed in order: palm oil = corn oil &gt; fish oil &gt; coconut oil (p &lt; 0.05). The particle size of the nanoemulsion (initial particle size = 168–185 nm) was below 200 nm during 42 days’ storage at 25°C. The retention rates of beta‐carotene in nanoemulsions were 69.36%, 63.81%, 49.58%, and 54.91% with palm oil, coconut oil, fish oil, and corn oil, respectively. However, the particle size of the nanoemulsion increased significantly in the accelerated experiment at 55°C (p &lt; 0.05), in which the retention rates of beta‐carotene were 48.56%, 43.41%, 29.35%, and 33.60% with palm oil, coconut oil, fish oil, and corn oil, respectively. From above, we conclude that WPI‐stabilized beta‐carotene nanoemulsion with palm oil as the carrier is the most suitable system to increase bioaccessibility and stability of lipid‐soluble bioactive compounds such as beta‐carotene