Triboelectric nanogenerators (TENGs) are devices that can harvest energy from mechanical motions; such devices can be used to power wearable sensors and various low-power electronics. To increase the lifetime of the device, scientists mainly use the method of making TENG in a hard skeleton to simplify the complex possible relative movements between two triboelectric parts. However, the hard skeletons cannot be embedded in soft and lightweight clothing. To make matters worse, the materials used in the garments must be able to withstand high mechanical forces when worn, such as the pressure of more than 100 KPa exerted by body pressure or everyday knocks. Notably, the TENGs are usually made of fragile materials, such as vacuum-evaporated metal electrodes and nano-sized coatings, on the contact interface; these electrodes and coatings often chip or wear off under the action of external loads. In this work, we succeeded in creating a thin, light-weight, but extremely robust garment-integrated triboelectric nanogenerator (G-TENG) that can be embedded in clothing and pass the water wash test. First, we chemically deposited a durable electrode with flexible properties for G-TENG using a novel technique called polymer-assisted metal deposition (PAMD). The as-formed metal electrodes are firmly bonded to the plastic substrate by a sub-10 nm adhesive polymer brush and can withstand a pressure of 22.5 MPa and a tear force of 0.7 MPa. We then removed the traditionally used fragile nanoparticle materials and the non-durable poly-dimethylsiloxane (PDMS) layer at the triboelectric interface, and then used a cost-effective, durable and slightly flowable pressure-sensitive adhesive to form a plastic contact interface. Such a soft plastic interface can ensure full contact of the triboelectric materials, which is excellent in complex environments and ultimately improves the power generation efficiency of the devices. The as-formed low-cost energy harvesting device could become an industry standard for future smart clothing. Triboelectric nanogenerators (TENGs) ,3,4,5,6 However, wearable and flexible TENGs usually suffer from low durability and short device lifetime. The most obvious reason is that the electrode material used for TENGs is not durable because the metal electrodes of TENGs are usually deposited by vacuum evaporation techniques, which lack an adhesive layer and are easily delaminated from the plastic substrates when the whole device undergoes large deformation .Another reason may be the fragility of the nanomaterials and deformable materials used in the triboelectric interface region of TENGs.Addition of plastic and soft material is necessary for TENGs because the surfaces of normal objects are rough, which prevents full contact between two triboelectric layers; the presence of many raised structures can make it difficult for a contact interface to achieve full contact without the assistance of the deformable features of plastic materials. In addition, airborne particles or dust  can be deposited on the surfaces of two triboelectric materials and prevent full contact.Therefore, many scientists choose to use a soft triboelectric layer, e.g., polydimethylsiloxane (PDMS), to fabricate a triboelectric interface . The PDM2/s2 during machine washing or pressures of >100 KPa that can occur when the garment is worn .However, the key situation is that both nanoparticles and PDMS thin film are fragile; the nanoparticles are easily worn out after repeated triboactivations, while the soft PDMS thin film is prone to delamination or detachment when the device undergoes large deformations. The properties of the triboelectric interface lead to damage, breakage, or detachment of the soft material  when acted upon by the large mechanical forces that the clothing material must bear, such as torques larger than 8 kg·mWe know that the intrinsic surface energy of PDMS is very low , it is dTherefore, replacing the existing materials is the only practical option to increase the life of the device. In this article, we propose the use of different materials to increase the life of a flexible G-TENG.In manufacturing the flexible and durable G-TENG, we started by replacing the original vacuum-coated electrodes with new highly adhesive and durable electrodes prepared by the chemical solution method, namely polymer-assisted metal deposition (PAMD) techniques ,29,30,31In addition, this reported solution method for the preparation of metal thin-film electrodes at room temperature has many advantages over metal thin-film electrodes prepared by the now widely used method of printing metal thin-film electrodes, i.e., printed metal nanoparticle ink technology , becauseAlthough the PAMD technique has great potential in production of high-quality TENGs, the existing report is somewhat stereotypical; it only reports application of this technique in production of textile-structure-based TENGs ,35, whicConsidering the above reasons, our TENG devices turn fiber-to-fiber contact into wide face-to-face contact; the PAMD-based metal electrodes were grown on a uniform large area rather than growing electrode coatings that wrap around fibers or yarns. The advantage of this design is that the enormous external pressure is distributed over a larger contact area rather than being concentrated on a small contact area. As such, this design provides possibility for application of PAMD in the field of durable and washable TENGs.In addition, the actual power generation mechanism of the TENG needs further research. The electrical energy of the TENG device is generated in the contact area, but, in recent years, the progress of research on the contact area of the TENG has been relatively slow. Only in 2020, the concept of the real contact area was emphasized by Xu et al. , and, inSoft materials are always used at the contact interface of the two triboelectric materials in order to increase the real-contact area ; by incrSecond, we redesigned the materials used to make the triboelectric interface of the TENG; we removed the fragile nanomaterials in the contact interface. We then used a low-cost but more durable pressure-sensitive adhesive to replace the brittle and easily detachable PDMS material, with the aim of creating a reliable contact interface that can adapt to complex environments and increase the output current of the device, which is another key point of our article.The soft pressure-sensitive adhesive will automatically fill the uneven surface area of the opposite substrate during each contact; the softer pressure-sensitive adhesive will flow into the concave part of the micro-nano gap on the surface of the opposite ordinary polymer material to achieve full contact. The real friction situation can be more complicated than imagined. In everyday life, there are various impurities or dust that fall on the surface of clothing. A soft and durable pressure-sensitive adhesive with certain flow characteristics will wrap around any impurities or protrusions so that the actual contact area is close to the ideal plane of actual contact.Working in conjunction with the chemically produced durable metal electrodes, the resulting soft and light-weight TENG was extremely durable and could be adapted to complex mechanical motion environments, e.g., working in motion in a combination of contact separation mode and sliding mode. In addition, the flexible TENG could be sewn directly into clothing to generate electrical power for many possible low-power electronics.As a proof of concept, we carried out a detailed analysis of the mechanical and electrical properties of the chemically prepared metal electrodes and found that the as-formed copper electrode has good adhesion to the plastic substrate and can pass the pull-off test. The as-formed metal coating will not break under a maximum tear force of 0.7 MPa and pressure of 22.5 MPa. At the same time, it has excellent conductivity and low surface roughness. The sheet resistance of the electrode can reach about 1 Ω/□. The root-mean-square roughness (Rq) of the electrode was measured to be ~3 nm, and the conductivity of the electrode gradually became constant during the 36 h long-term continuous bending test, the bending radius being as small as 1 mm. In addition, a soft material, i.e., the pressure-sensitive adhesive, was used to replace the PDMS as the triboelectric interface in order to increase the real contact area of the interface. This material was found to be stable when subjected to bending or water wash test. In the water wash test, the degradation of the output performance dropped by less than 20%, and, beyond this initial degradation, the performance was found to be stable again. Application of these high-strength film materials provides a good technical model for future mechanical energy-harvesting units that can withstand high impact or twisting and be embedded on the surface of clothing.The PE film (with its adhesive layer) and the 50 µm thick PET film were first bonded together. In this way, two triboelectric layers were bonded together to protect the contact interface from exposure to the chemical solutions during the growth of metal electrodes. This sandwich structure, two triboelectric films with a pressure-sensitive adhesive in the middle, was then dipped directly into the chemical solution for chemically deposition of the metal electrodes.The hydrophilic surfaces of the PET and PE substrates were first subjected to an oxygen–oxygen plasma treatment for 4 min. They were then immersed in a 3-(trimethoxysilyl) propyl methacrylate (MPS)  alcohol solution for 1 h. During this time, an MPS-based self-assembled monolayer (SAM) formed on the substrate surface. After ethanol rain treatment and subsequent drying, the SAM-modified substrate was obtained. Subsequently, the polymer brush of 2-(methacryloyloxy) ethyl-trimethylammonium chloride (METAC)  was grown on the acrylate group of MPS in an aqueous solution  at a concentration of 20 wt% mixed with the initiator potassium persulfate (KPS) . The METAC was grown at 80 °C for 1 h. The PMETAC-modified substrates were then rinsed with deionized water and dried by spraying with pure nitrogen. An aqueous solution of 5 mM ammonium tetrachloropalladate (II) was used to treat the polymer-brush-based copper electrode for 30 min. Electroless metal deposition was then carried out using a freshly prepared electroless deposition (ELD) solution. In this process, the thickness of the metal electrode can be controlled by varying the duration of the metal electrode deposition.PET was chosen as the electron acceptor layer for the formed TENG, and a low-cost rubber (poly-cis-isoprene) adhesive  was used as the electron donor layer ,42. FirsUnlike the thermally evaporated metal electrode, which has no bonding material between the electrode and the plastic substrate, the chemically prepared metal electrodes have a polymer brush layer grown by Atom Transfer Radical Polymerisation (ATRP) technique under the metal electrode, which can provide a strong adhesion force for the metal electrode.Here, we prepared a highly conductive and durable metal electrode in solution environment. In this process, two triboelectric materials were firstly first bonded together by using a pressure-sensitive adhesive, with only one side of the plastic dielectric layer was exposed to the chemical solutions, as shown in The oxygen plasma treatment was applied to the two exposed dielectric layers to increase the number of hydroxyl groups on the surfaces and their wettability to an aqueous solution. This step was necessary because the surfaces of the two dielectric layers had intrinsically different wettability as the PET molecule contains hydroxyl groups while the PE molecule does not   is an ef2− containing palladium metal catalyst anions gradually replaced the original chlorine ion paired with the cationic polymer brush during a long-term immersion process. Ion replacement was achieved in a 1 mM aqueous solution of ammonium tetrachloropalladate (II) over a period of more than 20 min.The cationic polymer brush was used to immobilize the catalyst anions of ammonium tetrachloropalladate (II), as shown in Finally, the immobilized palladium metal ions were used as a catalyst to deposit copper on the polymer brush; in this way, the copper formed a strong and reliable thin-film electrode on two dielectric layers and built up all the necessary components of the TENG device f.As shown in Although researchers typically deposit copper electrodes by vacuum deposition or attach the copper tape directly to the triboelectric layer to form a TENG, electrodes grown by chemical methods have unparalleled advantages. The main advantage of this technique is its ability to produce an ultra-thin adhesive layer (only 1–10 nm thick) , a polymPassing the adhesive tape test is the minimum requirement for the adhesion force between the metal electrode and the plastic substrate for our adhesive-tape-based TENG because the adhesive is also used as the contact interface. If the electrodes we manufacture are not strong enough, the entire device will fail during the continuous bonding and separation of the two triboelectric parts.Fortunately, the electrode produced by this chemical method  can easily pass the adhesive tape test . In thisThe chemically prepared copper coating on the PET film was found to be extremely resistant to damage. As shown in PDMS, the material mainly used to fabricate deformable triboelectric interface, is a polymeric organosilicon compound that has extremely low surface energy; as such, this material does not bind reliably to other materials. It is common for PDMS to become detached from substrates to which it is bonded during large deformations of the substrate. To make matter worse, PDMS is not strong and is easily shattered by strong impacts. Rubber (poly-cis-isoprene), rubber has many advantages and is likely to replace PDMS as a high performance, low-cost triboelectric interface. Rubber is a polymer synthesized from petroleum by-products. This durable and soft material inexpensive and widely available; it could be an ideal replacement for the PDMS typically used in TENGs.Pressure-sensitive adhesive made by rubber shows good adhesion to PE films after hot-pressing treatment. After such treatments, the rubber rarely separates from the PE film. The adhesive used here also has excellent flexibility and conforms to the shape of the opposite surface of the PET, resulting in a large real contact area, as shown in The surface of most interfaces is rough; there are tiny bumps on the surface of ordinary objects, although the raised points are difficult to see with the naked eye or optical microscopes at low magnification, as shown in Unfortunately, the presence of the raised points and particles on the surface of the triboelectric material prevents good contact between the two triboelectric materials. It is hypothesized that at least one of the friction materials must be soft in order to create a good fit with the second material through small deformations induced by external pressure. These deformations can lead to a significant increase in the real contact area at the interface. As shown schematically in As a proof of concept, we carried out an experiment to compare the power generation of TENGs built with and without the presence of the pressure-sensitive adhesive in the contact area.Our test involves manually contacting and separating the two triboelectric parts.The electrical signal was collected using a source meter (Keithley 2400 Sourcemeter). 2. The instability of the experimental signal may be due to the introduction of the biological signals when the finger touches the TENG electrodes to complete the experiment.As shown in Compared to the PDMS often used in TENGs, the adhesive used here has higher adhesive strength to bond the two elements of the TENG. It is, therefore, relatively difficult to separate the two elements, especially in the case of wearable and flexible triboelectric films. When the tear speed is low , a unique output current pattern is generated, as shown in The existence of this increased adhesion between two triboelectric parts indicates that the two triboelectric materials are in good contact and that significant van der Waals interactions are formed between the surfaces, thus maximizing power generation. The formed TENG requires a slightly greater external force to separate the two triboelectric parts in the power generation process compared to PDMS-based devices, but the power that can be generated is also increased. This eliminates the need for an large external force to press the two friction plates together. The two triboelectric parts are easily bonded by the presence of a pressure-sensitive adhesive. Only a little more force is required to separate the two friction plates, but the increase in separation force is still small compared to forces generated by the movement of human limbs. This wear discomfort will be reduced with increased use or embedding of external contaminants, or with the invention and application of new low-adhesion pressure-sensitive adhesives. Increased adhesion will, therefore, not have a significant impact on wearer comfort.Washability is also a significant issue in G-TENG applications. The washing process is often accompanied by high torque, shear, and abrasion stresses that can damage or destroy the brittle structure of common electronic devices .Here, the TENG device as designed with the use of the durable chemically deposited metal electrode and the durable triboelectric interface, has an advantage in terms of washability. As a proof of concept, we tested the washability of our proposed TENG using a washing machine , working in normal mode (45 min with washing and spinning). The current output value was maintained at ~80% of the original value after the first cycles of machine washing and drying, as shown in The TENG developed here can be used to power various electronic devices, such as health sensors and signal antennas, embedded in smart clothing, as shown in the diagram of potential applications . It can 2/s2 during machine washing, and pressures >100 KPa . We have pioneered the use of extremely durable materials in the manufacture of thin-film G-TENGs to extend the life of the device. In the manufacturing process, we first replaced the traditional thermally evaporated metal electrode with a novel durable electrode produced by chemical methods. The as-formed thin-film metal electrodes were electroless deposited onto an ultra-thin polymer-brush-based adhesive layer (1–10 nm thick) pre-embedded in plastic substrates. This paper focuses on the durability of the material itself and the high performance of the conductive electrodes, which remain robust without cracking or delaminating under a pressure of 22.5 MPa and a tear force of 0.7 MPa. In addition, the electrodes also have a smooth surface morphology and excellent electrical conductivity. We then used a low-cost but durable pressure-sensitive adhesive to create a reliable contact interface that can adapt to complex environments and increase the output current of the device. Due to the high deformability of the pressure sensitive adhesive, each friction of the TENG forms an almost complete fit at the triboelectric interface. This means that the real contact area for each contact is close to the maximum. The TENG produced here also exhibited high electrical stability under large mechanical deformations during the washing test.Flexible G-TENGs harvest electrical energy from people’s body movements, but they lack durability. Although hard skeletal structures can be used to extend the life of the device, they are not compatible with thin and flexible clothing. However, clothing materials must be able to withstand high external forces, such as torques exceeding 8 kg·mAt the same time, the process is cost-effective because the entire electrode manufacturing process takes place under ambient conditions. Extreme preparation conditions such as high temperature or high-energy curing environments are not required throughout the manufacturing process. The energy-saving nature of this technology can help the future industry of electrode preparation to adapt to the time of energy crisis.With the low production cost and energy consumption, this reported durable and flexible G-TENG could become a reliable and necessary energy harvesting unit for future smart clothing.


Morphing aircraft are capable of modifying their geometry configurations according to different flight conditions to improve their performance, such as by increasing the lift-to-drag ratio or reducing their fuel consumption. In this article, we focus on the airfoil morphing of wings and propose a novel morphing control method for an asymmetric deformable airfoil based on deep reinforcement learning approaches. Firstly, we develop an asymmetric airfoil shaped using piece-wise Bézier curves and modeled by shape memory alloys. Resistive heating is adopted to actuate the shape memory alloys and realize the airfoil morphing. With regard to the hysteresis characteristics exhibited in the phase transformation of shape memory alloys, we construct a second-order Markov decision process for the morphing procedure to formulate a reinforcement learning environment with hysteresis properties explicitly considered. Subsequently, we learn the morphing policy based on deep reinforcement learning techniques where the accurate information of the system model is unavailable. Lastly, we conduct simulations to demonstrate the benefits brought by our learning implementations and validate the morphing performance of the proposed method. The simulation results show that the proposed method provides an average 29.8% performance improvement over traditional methods. While unmanned aerial vehicles (UAVs) have played a crucial role in various civil and military missions, studies demonstrate that birds usually possess higher flight maneuverability and agility than comparatively-sized aircraft in complex and varying environments ,2. One oThe ideal airfoil is usually generated using shape optimization techniques including gradient-based methods and gradient-free methods , which oAlthough an optimized airfoil shape can be calculated given a certain flight condition, it is challenging to morph into optimal shapes instantaneously during the flight procedure due to the uncertainty and inconsistency of the environments and aircraft dynamics ,21. ThisConcerning the realization of morphing mechanisms, biologically inspired mechanical joints are often adopted to control sweep-, dihedral- and twist-morphing wings ,28,29. HIn this work, the morphing control for a deformable asymmetric airfoil based on deep reinforcement learning techniques is investigated. The contributions of this work are threefold, as follows. Firstly, the shape of the asymmetric airfoil is designed based on Bézier curves, and the morphing mechanism of such airfoils is modeled via SMA wires. Subsequently, a dynamic system between input voltages and airfoil shapes is developed, which characterizes the hysteresis behaviors in the manner of Markov decision processes. Finally, the morphing policy is constructed based on deep reinforcement learning approaches without accurate knowledge of system models, which adjusts the airfoils during flight procedures to track the optimal shapes in different flight conditions.The rest of this paper is organized as follows. In N control points, which are connected by Bézier curves [N sectors equally, in each of which a control point is placed. These points are sorted with respect to the azimuth and denoted as Well-known methods of curve synthesis for airfoil design and optimization include splines  ,43, freer curves ,20. To gAn example of a valid shape is illustrated in Denote the polar coordinate of each point Since there has been a variety of investigations on the position control of DC motors , we assuT denotes the temperature, and w, h is selected to ensure the intersection of the consecutive curves at the current point and is given bySmart materials, especially shape memory alloys (SMA), are adopted to realize the airfoil morphing, where the radii of control points are modified via adjusting the length of SMA wires. Firstly, the dynamic model is constructed between the wire temperature and radii. An SMA wire changes its length through the crystal phase transformation between martensite and austenite according to the temperature. The transitions to martensite and austenite have different start and end temperatures, which leads to the hysteresis properties of the strain with respect to the temperature. Instead of common methods such as Preisach model and Krasnosel’skii–Pokrovskii model , the SMAa of SMA ,41. The An illustration of the transformation procedure is given in T follows the heat transfer model [After constructing the temperature-strain model, resist heating is used to actuate the SMA wires . Given ter model 10)mwcwmwcwvi, tIn this work, we tackle the morphing problem for the airfoil constructed by the SMA wires whose dynamics are given in –10). No. No10). S and A are the state space and action space containing all the states and actions, respectively, R is the reward function giving P is the transition function giving Reinforcement learning (RL) methods are capable of learning a control policy from interactions between the given agent and environment, with no requirement on the knowledge of system models . The leaBefore choosing the states and actions for the morphing problem, the morphing system is investigated further to construct an MDP from it. Firstly, the voltage-temperature dynamics  of SMA wfunction  as fl and  as fr  9) that T and h as states and v as actions to construct a RL environment for airfoil morphing. The radius factor can be treated as an observation since it is dependent on only the current voltage, temperature and height parameter. Since the length and temperature of SMA wires can be measured directly via sensors equipped on the airfoils, these values are assumed to be available. However, the height parameter is not a realistic physical characteristic but just a coefficient fitting the hyperbolic tangent curves to the actual SMA properties, which makes the measurement on h not available. Actually, we aim to adjust the position of control points to achieve a reference airfoil shape, such that we want to learn a policy on modifying the lengths . We observe from the loop functions  an an15) anunctions  and 8)  T and h tionship  as γi,k=Combining  and 18)18), the Note that Proposition 1.Given a second-order MDP with states select then the MDP constructed by s and a satisfies the transition functionThis proposition can be derived directly via the properties of MDPs. According to Equations , 19) an an19) ank is designed byThe reference radius factors of the optimized airfoil shape under certain condition After establishing the MDP, we proceed to tackle the morphing task based on deep reinforcement learning techniques. Our learning method is designed based on the soft actor-critic (SAC) algorithm , which iWith regard to the construction of the action-value function, instead of directly applying , we augmrding to , we defiAfterwards, the Bellman equation for the action-value function is given ase of SAC , we apprB is the sampled batch with elements For the learning of the action-value functions, the double-Q trick is applied to avoid overestimation . Two crik is composed of the input voltages at k, the actor network should be designed as Subsequently, we aim to find the policy that maximizes the expected action-value function with respect to the actions. Denote the parameters of actor network as J denotes the Jacobian matrix.The reparameterization trick is adopted here for the sake of the efficient computation of gradients . We intrction asvk=Vmax21 formula  as(37)lWith Then, we conduct the training by updating the actor and critic networks iteratively. The agent interacts with various randomly generated time-varying reference shape sequences to acquire training data, which faciliates the exploration and enables the policy to handle different morphing scenarios. When the training converges, we can use the actor network to calculate the required voltages and morph the airfoil to the reference shapes. Finally, the overall flowchart of the proposed morphing mechanism is given in In this section, a simulation is conducted to validate the proposed morphing method. The simulation is arranged in two stages. Firstly, we implement our method with random generated reference shapes and perform ablation studies to examine the superiorities brought by different parts of our algorithm. Subsequently, we apply the proposed method to track optimized airfoil shapes in different flight conditions and show the morphing procedures. The values of parameters in our simulation are given in In this stage, the superiorities of our method are illustrated in a variety of perspectives, including the state/action selection, reward configuration and entropy regularization. Without loss of generality, piece-wise constant trajectories of the radius factor for one control point are generated to represent the reference shapes. Then, our method is compared with three different settings of RL algorithms. The proposed method is denoted as RLM-SAC.Second-order state/action versus first-order state/actionIn Sparse reward versus squared error rewardWe designed a sparse reward taking value in SAC versus DQNThe entropy regularization improves the capability of exploration in our algorithm. A modified deep Q learning method was implemented as a comparison, where only the entropy loss was removed, and both the double-Q setting and reparameterization trick remained. We denote this as RLM-DQN.All RL realizations were trained through 150 epochs, in each of which 5 episodes with 40 s of time and 200 time steps were executed. The critic network was constructed using multilayer perceptrons (MLP) of 3 hidden layers and 128 units per layer. The actor network adopted similar structures, where additional fully connected layers were attached to produce the mean and standard variations of the policy. The training was started with actions uniformly sampled from the valid action space bounded by The results are shown in From the results, we can see the benefits of each important component in our algorithm. Firstly, RL using square error reward totally fails to produce effective actions in our environment, which is shown from both the RMSE and example trajectories. RLM-FO acquires inferior performance compared with RLM-SAC, especially in the temperature-switching procedure, which can be validated by the middle sections of the example trajectories. This is a result of the fact that the hysteresis cannot be characterized well by first-order states and actions. Lastly, RLM-DQN obtains better performance than RLM-FO and RLM-SRE, and it achieves a similar reward to RLM-SAC at the end of training. However, from the illustrations of rewards in In this stage, the trained actor network is applied to morph an airfoil controlled by four points in a given flight procedure with varying flight conditions. Since the focus of this work is morphing control, in each condition, the optimal shape is assumed to be solved in anticipation by shape optimization techniques and determined by the radii and angles of control points ,18,20. TSince the position control technique of motors is relatively sufficiently developed, we assumed that the angles of the points are controlled by DC motors with accurately known linear dynamic models, which can drive the points to desired angles rapidly with subtle errors. Then we generate input voltages to heat the SMA wires and adjust the radius factors. The voltages, temperatures and radius factors of all points are summarized in Furthermore, we illustrate the morphing procedures in Quantitative comparisons on the length factor differences and shape differences are given in The difference between the actual and reference shapes are evaluated using distances between the control points. An average shape difference over all time is calculated asIn this work, a novel deep reinforcement learning-based morphing control method is proposed for an asymmetric morphing airfoil. The airfoil is designed via Bézier curves and is capable of morphing from a baseline shape to an asymmetric shape. The morphing mechanism is modeled via SMA wires, which adjust shape parameters, especially the radii of the control points. To actuate the SMA wires, resistive heating is performed, but the hysteresis characteristics between the SMA strain and temperature make the dynamic system nonlinear and non-Markovian, which brings difficulties to the design of the control algorithm and the RL framework. Therefore, hyperbolic tangent curves are adopted to model the strain-temperature relationship and derive a second-order MDP describing the system, which is then transformed into a valid MDP and provides guidance for the selection of states and actions. Based on the constructed MDP, we modify the SAC algorithm and develop an RL scheme where input voltages are generated to morph the airfoil instantaneously according to reference-optimized shapes. Lastly, ablation studies on random generated reference trajectories are conducted to demonstrate the benefits brought by different components of our RL implementations, and we perform simulations of morphing procedures to validate that our method is able to morph the airfoil into the optimized shapes rapidly and accurately. Future works include incorporating the aerodynamic performance optimization directly into the morphing control and exploiting learning-based morphing policies for more complicated bio-inspired morphing aircraft.


Conclusions: The results suggest that motivational interventions can improve the symptoms because their objectives are focused on managing conflict goals. These interventions should focus on catastrophic cognitions considering that pain catastrophizing is deemed to be one of the major inhibitors of physical activity in fibromyalgia.Background: Fibromyalgia is characterized by chronic pain and fatigue that triggers a functional disability caused by the lack of activity. Pain catastrophizing may contribute to avoiding activity with the intention of managing pain levels. Based on the sedentary behavior with fibromyalgia, the present study assessed the preference of pain-avoidance goals and pain catastrophizing as mediator and moderator variables, respectively, that influence pain perception after a 6-min-walking test. Methods: The sample was composed of 76 women with fibromyalgia . Previous sedentary behavior, preference for pain-avoidance goals, and pain catastrophizing were evaluated before starting the walking-test. Subsequently, pain perception was evaluated. Results: A significant moderated-mediation model was found in which pain-avoidance goals mediated the relationship between sedentarism and pain after a walking-test, and pain catastrophizing moderated the relationship between the preference for pain-avoidance goals and pain perception. Specifically, high levels of pain catastrophizing contributed to increased pain perceptions after completing the test (B = 0.570,  Fibromyalgia (FM) is a physical condition characterized by chronic widespread pain, often associated with poor sleep, fatigue, depression, reduction of physical functionality, and with a multitude of personal costs that generate suffering in patients ,2. AmongHowever, focusing the therapeutic efficacy on reducing pain severity through inactivity is an insufficient long-term goal . ChronicDue to the complexity of the disease, it is important to develop and implement multidisciplinary treatments that are methodologically rigorous, focused on reducing the impact of FM ,12. DespPrevious studies suggested that women with FM are less active compared to people without previous pathologies ,18. PrelEven though walking behavior is a pattern adapted to the condition of FM patients, physical dysfunction continues to be a main problem preventing an improved quality of life of these patients . TraditiIn neurobiological terms, catastrophic beliefs about pain act as anticipatory to the painful sensation. This translates into the rapidity of the dorsal horn activation, so that painful information is transmitted in an accelerated manner in top-down processes ,28,29. PHowever, the link between pain catastrophizing and pain is more complex because other contextual variables significantly influence this relationship . In the Despite the support received from motivational theories explaining the maintenance of pain and lack of adherence to walking patterns in FM patients, there are few studies on the influence of goals on symptoms such as pain or fatigue in chronic pain pathologies, as well as the role of pain catastrophizing in goal conflict and the deterioration of symptoms. Therefore, the main objective of the present study focuses on the exploration of the influence of a sedentary lifestyle on pain severity in terms of the preference for pain avoidance goals and the level of catastrophizing controlling the effect of pain chronicity. In summary, we hypothesize that the most sedentary FM patients tend to meet goals related to avoiding activities that enhance pain . In turn, patients who avoid more activities because of the pain are likely to perceive more intense pain after walking depending on the level of pain catastrophizing. That is, patients who display more avoidance behaviors will perceive more pain perception after activity if their thoughts about pain are more magnified . The results obtained in the proposed model may serve as a basis for future studies aimed at designing rehabilitative treatment programs, considering the conflict of personal goals as an influential variable in the health behaviors performed by patients with fibromyalgia.–Occipital insertions of the suboccipital muscles;–Anterior cervical projection of the C5–C7 intertransverse spaces;–Midpoint of the upper border of the trapezius;–Origin of supraspinatus;–Second chondrosternal junction;–Two centimeters distally from the epicondyle;–Upper outer quadrant of the buttock;–Posterior aspect of the greater trochanter;–Adipose cushion of the inner aspect of the knee.Participants were recruited voluntarily from different associations for fibromyalgia in the Community of Madrid and Castilla-La Mancha (Spain). All of them had previously met the diagnostic criteria for FM established by the American College of Rheumatology . Some of the patients were diagnosed with the 1990 ACR criteria: (a) chronic widespread pain of more than 3 months duration affecting at least 3 of the 4 quadrants of the body; and (b) pain or pressure from 11 of the 18 points chosen:Other patients were diagnosed with the 2010 ACR criteria consisting of a Widespread Pain Index (WPI) and a Symptom Severity Scale including the following: fatigue, unrefreshing sleep, cognitive problems, and somatic symptoms  ,37. DespThe study was approved by the Bioethics Committee at Rey Juan Carlos University (Reference PI17/00858). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration. All participants signed informed consent forms and gave permission to use their evaluation data for research purposes. To preserve the privacy of our participants, each patient was assigned a numerical code to prevent recognition by the research team.This study is part of a larger project consisting of three phases. Specifically, this study is part of the third phase that focuses on the study of pain catastrophizing and the conflict goals related to pain in walking behavior. Therefore, a design based on an observational analytical laboratory study is proposed.First, a preliminary evaluation was carried out to obtain information on the demographic and clinical characteristics such as the type of medication administered, or the time elapsed since the fibromyalgia diagnosis. Self-report questionnaires  were also administered to evaluate the variables of interest. Subsequently, participants were asked to perform the Six-Minute Walk Test (6MWT). The purpose of this test is to measure the maximum distance a person can cover during a period of 6 min walking as fast as possible. The instruction given to each individual participant is to walk as fast as possible and as comfortably as possible for the stipulated time. The 6MWT is a test frequently used for fibromyalgia patients and recommended by the Spanish Society of Rheumatology for Fibromyalgia ,40. At tInitially, 90 women with fibromyalgia were contacted, of whom 76 completed the evaluation protocol and performed the walking test.Sociodemographic and clinical data. First, open-ended questions were asked with the intention of collecting information from the participants, such as age, educational and employment level, marital status, time elapsed since the diagnosis of FM (years), and medication prescribed. All of these data were collected to describe the sociodemographic and clinical characteristics of the sample.Preliminary sedentary behavior. The short form of the International Physical Activity Questionnaire (IPAQ-s) is known to be a valid and reliable self-reporting instrument for measuring walking behavior and types of physical activity as a function of intensity levels (intense or moderate) in clinical populations (adults aged 15–69 years) ). The effect of a sedentary lifestyle on pain perception through avoidance activity preference was also significant (path b) . The total effect of the previous sedentary behavior on pain perception was significant (path c) . The direct effect (path c’) was significant and the simple mediation model accounts for 20.16% of the variance in pain perception after the 6MWT . In relation to the covariates, pain perception over the past week had an influence on the intensity of pain perceived by the patients after performing the walking-test . However, the time elapsed after receiving the diagnosis was not significant in the model . In summary, the results indicate that previous sedentary behavior promoted a preference for avoidance of pain-related activities. Furthermore, activity avoidance was associated with more pain after the 6MWT.As presented in p = 0.047; [CI = 0.017–0.035]) according to the preference for pain-related activity avoidance goals (path a) . The effect of the preference for avoidance goals on pain perception after the 6MWT was also significant (path b1) . In addition, the pain catastrophizing of FM patients moderated the effect of the preference for pain avoidance goals on pain perception after the 6MWT (path b3) . The results of the conditional indirect effects showed that the observed positive link was only significant if FM patients reported high levels of pain catastrophizing (path b2) . The preferences for pain-avoidance goals were not associated with pain perception if patients reported medium or low levels of pain catastrophizing . Similarly, the previous general pain reported by patients only influenced the effect of the preference for pain avoidance goals on perceived pain after the walking-test . No significant effect of general pain was found in preferences for pain-avoidance goals . The time elapsed since diagnosis had no effect on the proposed model . In summary, more sedentary women reported greater pain after the 6MWT due to preference for pain-related activity avoidance and significantly moderated higher levels of pain catastrophizing  (see 32]) see .Much of the research maintains that there is a greater tendency to a sedentary lifestyle in women with FM compared to healthy women . The maiIn accord with the literature, physical activity, sedentary behavior, and physical fitness are well-known determinants of health in FM ,51. As iHowever, as is well known, fibromyalgia is characterized by widespread pain throughout the body ,2. Thus,On the other hand, the model was confirmed regardless of the years of chronicity of the diagnosis. According to previous literature, patients with fibromyalgia often suffer the core symptoms of fibromyalgia, such as pain or fatigue, for many years despite not receiving a specific diagnosis by professionals at the time of symptom onset. This fact makes the symptomatology management and coping strategies generally maladaptive on the part of the women who do not receive supervision at the appropriate time  . In summFrom the models of fear of movement, most of the research has been carried out taking adherence to physical activity as the variable under study. However, from these same models, explanatory studies on the influence of sedentary lifestyle are scarcer. In this context, the achievement of certain goals related to physical activity is associated with increased pain and fatigue in chronic pain patients ,65. FollFear of pain is an influential variable in the preference for short-term hedonic goals. According to previous contributions, fear and avoidance is the result of a behavioral pattern that is not associated with the disease, and that leads to an exaggerated perception of pain ,68. In aThe present study has some limitations that should be considered. First, it should also be acknowledged that there was a relatively low sample size assessed in this study and, in addition, the sample is made up of women only, which prevents generalization of the results. However, this study’s sample is similar to the sample sizes of previous studies based on the 6MWT in patients with chronic pain ,70. The Despite these limitations, the results of the current study increase our understanding of the relationship between pain catastrophizing and the preference of different goals in FM from a motivational perspective about pain. One of the strengths of this study is the use of the 6MWT, which has been commonly used in FM patients as an objective measure of functional capacity ,70. On tIn summary, a multidisciplinary approach based on attention to psychological factors such as catastrophic beliefs about pain and internal conflict about avoidance vs. activity goals could help patients with FM to overcome their medical condition and cope with the difficulties associated with the disease. Since walking is an accessible, easy exercise with favorable health outcomes for patients, enhancing the intention to initiate this behavior as a rehabilitation guideline within the multidisciplinary context is encouraged. To this end, by exploring the catastrophism towards pain and fear of movement, which can act as a promoter of sedentary behavior, the intensity of these beliefs can be reduced so that the adherence to walking behavior might be increased.


This paper presents a dynamic segmentation and weighted comprehensive matching algorithm based on neural networks for cross-media piano performance audio recognition and retrieval. The 3D convolutional neural network process is separated to compress the network parameters and improve the computational speed. Skip connection and layer-wise learning rate solve the problem that the separated network is challenging to train. The piano performance audio recognition is facilitated by shuffle operation. In pattern recognition, music retrieval algorithms are gaining more and more attention due to their ease of implementation and efficiency. However, the problems of imprecise dynamic note segmentation and inconsistent matching templates directly affect the accuracy of the MIR algorithm. We propose a dynamic threshold-based segmentation and weighted comprehensive matching algorithm to solve these problems. The amplitude difference step is dynamically set, and the notes are segmented according to the changing threshold to improve the accuracy of note segmentation. A standard score frequency is used to transform the pitch template to achieve input normalization to enhance the accuracy of matching. Direct matching and DTW matching are fused to improve the adaptability and robustness of the algorithm. Finally, the effectiveness of the method is experimentally demonstrated. This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and storage module of cross-media piano performance big data, the building module of audio recognition of cross-media piano performance big data, and the dynamic precision module of cross-media piano performance big data. In recent years, deep learning has achieved great success in computer vision. 3D convolutional neural networks have been proposed to improve the recognition accuracy based on the recognition of dynamic gestures . The firTraditional cross-media information search is based on the semantic information processing of data in multiple media forms to build a public semantic space and map the data content of various media forms into it. In the public semantic space, supervised classification or unsupervised clustering of cross-media semantic features is performed by machine learning or deep learning algorithms, and feature matching is performed to realize cross-media search. For social networks, it is essential to perform feature mining for cross-media data with unique characteristics purposefully . In addiGiven the existing piano performance audio recognition and retrieval algorithms with many attributes, category subdivision, and difficulty in complete coverage and precise refinement, we use artificial intelligence deep learning technology and comprehensive association of piano performance data with multi-dimensional characteristic information and multiple category attribute information to study the extraction and expression of cross-media semantic features of piano performance entities based on deep learning and use piano performance-related data sites and networks as the research objects . Based oSince each essential play in piano performance can be regarded as a note event occurrence, note event detection is mainly responsible for detecting note event occurrence, including two subtasks: note start detection and note end detection. By extracting the start time and end time of each note event and considering each note event as a note event segment between the start and end time, complete piano music in WAV format is divided into multiple note event segments, each of which contains one or more notes played at the exact moment. Considering that the onset of some low-frequency messages is usually soft and it is difficult to detect the weak start after the intense onset by traditional methods, this paper proposes to use a combination of band segmentation weighting and power scaling and apply it to spectral flux, which not only enhances the weaker onset and suppresses the more robust onset in the music signal, but also enhances the robustness of the detection method; furthermore, by adding a window differencing process to increase the correctly detected. The overall detection performance is improved by increasing the correct detection of note onsets and reducing false detections through windowing.Existing cross-media retrieval approaches mainly model different media data to map them into a subspace and then perform similarity measures based on the representations of other media data in the same space . In pastAudio codec compression technology is mainly developed by ISO and IEC MPEG organizations. MPEG1 is the first high-quality audio data compression. MPEG1 can be used for up to two audio channels. It features optimal decomposition of the audio signal, adaptive quantization, and entropy coding of adaptive time-frequency blocks (ATFT). Most of the proposed solutions for audio copy detection are based on content fingerprinting . Audio, With the progress in the field of computer vision research, content-based retrieval techniques have emerged. Yao et al. proposed the combination of scale-invariant feature transform (SIFT) extraction of underlying features and hash retrieval to achieve fast image matching and improve the efficiency of image retrieval . Li et aFully connected layers implement the traditional neural network structure, and the model is computationally intensive, and the operation is time-consuming. With the introduction of deep learning, deep neural networks have been gradually developed. A deep neural network is a multilayer network structure that divides a target into multiple levels, making the semantic information obtained more sampled and diversified. A convolutional neural network adds a convolutional structure to a deep neural network, which is trained to determine the weights of each connected layer. It then processes the input image to output the category information. The more layers of hidden layers of CNN, the more profound and more complex the network is. Convolutional neural networks have been applied to various fields, and the structure and training algorithm of convolutional neural networks are described below. Convolutional layer: inputting images into a trained CNN will give predicted results. There can be no definite mathematical relationship between the input and output, and its main structure is the convolutional layer. For the input image, the first step is to go through the convolution layer, and the image is viewed as a matrix. The convolution kernel, also called filter, will be preset when doing the convolution operation. The matrix of the same size as the convolution kernel will be selected in turn on the input image to perform the inner product operation with the convolution kernel. The convolution calculation process is shown in H × W, a convolution kernel of E × E, several complementary zeros of N, and a convolution step of S , the output matrix H1 × W1 isEach convolution kernel extracts only a small portion of the image features, which is also more in line with the way humans process images from a bionic point of view. When humans observe things, they do not need to perceive all aspects, but only the valuable information can be understood, and the elements that are farther away are less relevant. The extracted features are fed into the activation function to obtain the feature map. Different filters are used, and other feature maps are obtained. Usually, after the convolution operation, the size of the output feature map will be smaller than the size of the input image. To make the feature map size more prominent, the input image matrix can be filled with zeros around it, called padding. For an image with matrix size n times in each dimension. The everyday pooling operations include average pooling, random pooling, etc. Max Pooling is to select the maximum value of a local image block to represent the local area, just as a school principal can designate a school. Average Pooling is to add all the importance of an image together and take its average to represent the local area, where each value in the local area plays a role. Random pooling is to normalize the values of the input feature map to get the probability value of each deal and then randomly perform the extraction. The larger the probability value, the easier it is to be selected; this pooling method can effectively prevent overfitting. In practical applications, people tend to care more about the most noticeable features in the local area, so the maximum pooling method is used more often. Deep learning methods are used in cross-media piano performance audio recognition and retrieval algorithms, making full use of their abstraction ability to learn correlations between different modal media, and proposing a cross-media retrieval method based on compressed convolutional neural networks to improve retrieval speed on the basis of guaranteed retrieval accuracy in response to the problems of computationally intensive and slow deep neural network models.Pooling layer: pooling is used to reduce the size of the image. In the extracted feature map, the values between adjacent positions are usually very similar, so the feature map is reduced in dimension without losing too much image information, which prevents the network model from overfitting the training data and reduces the number of parameters of the network model . The inpConnected layer: the fully connected layer is at the end of the convolutional neural network and plays the role of classifying the input. The input image has already been convolved, activated, and pooled, and the fully connected layer integrates all the information for feature classification. In the fully connected layer, the computation process is as follows:Sensation segmentation algorithms based on skin color, contour, motion information, texture, and other information have been studied in-depth and applied to different environmental scenes. Skin color is a feature carried by the human body itself. Skin color-based gesture segmentation becomes the most functional algorithm when the contrast between background and skin color is prominent. In addition, the algorithm has the advantages of simple implementation and high execution efficiency . Before The gesture is the primary motion target in the video, and the background is generally stationary. The gesture can be located and segmented from the ground by some motion target detection algorithms. Background subtraction, frame difference, and optical flow methods are the relatively simple and most used motion target detection algorithms. The background subtraction method starts by building a background model. The simplest method is to use the image without the target to be detected as the background and then subtract the pixels of each frame of the video from the background image pixels to find the difference. When the absolute value of the difference is more significant than a predefined threshold, the pixel is determined to belong to the target. Besides, researchers have also proposed some background modeling algorithms, such as the Kalman filtering method, hybrid Gaussian modeling method, and statistical averaging method. The principle of the frame difference method is similar to that of the background subtraction method. The difference is that the frame difference method is to make a difference between adjacent frames in the video. The following equation can express the principle of both. The main challenge of media retrieval is how to solve the problem of data distribution and inconsistency of representation among different media types of data. To this end, the implementation of cross-media retrieval techniques requires the support of multiple technologies, including computer vision, natural language processing techniques, and artificial intelligence.As a unique feature of human hands, compared with other segmentation methods, gesture segmentation based on contour information can avoid the effects of different racial skin tones and is also robust to illumination changes. The widely used methods are edge detection and contour template matching. The edge detection method obtains the edge of the gesture by the edge detection operator, mainly applicable to images with simple backgrounds. The background edges will enormously interfere with the gesture edges when the environment is more complex. On the other hand, the template matching method locates the gesture by traversing a predefined gesture template over the image to be detected and calculating the match with all the targets on the image. However, due to the variety of gestures, which makes no fixed template for motions, and the high computational cost brought by traversing over the whole image, research scholars gradually give up using the template matching method for gesture segmentation. Gesture segmentation algorithms are also based on active contour models, visual saliency, and entire convolutional neural networks. They have their advantages and disadvantages and are generally a combination of multiple segmentation algorithms to achieve complementary benefits. The advantage of LSTMs over traditional RNNs is that they retain information for more extended periods, allowing for learning earlier information about the sequence, which can significantly impact model decisions. Music is formed by the sequential combination of multiple notes in a time series and the sequence processing properties of LSTMs. This study will apply LSTM networks to learn and train the time series note data and eventually fit music with repetitive melodic structure.The fundamental frequencies obtained by different people humming the same note are different. To make matching under the same criteria, in any case, it is proposed here to use the piano note fundamental frequency as a template, transform the music score into a piano pitch template, and normalize the input music and the piano template by using two templates, the piano template and the input pitch template, which are identical in geometric change trend, for matching, breaking the original graded the idea of matching. The correspondence between standard piano pitches and fundamental frequencies is shown in The problems of start point alignment and note division errors are also avoided. First, the user must hum at least one whole phrase when humming, so a certain length of the template is assumed. Also, the piano templates stored in the database are obtained by dividing the song into whole phrases of variable length. Only full expressions of similar size to the number of notes input are considered, and only the maximum similarity of the entire song is kept. However, when significant errors are encountered in note segmentation, even if the notes that differ between the whole phrase and the input are ignored, it will still result in a minor similarity. The improved DTW algorithm allows the matching of similar profiles and even enables some offset of slices on the time axis. Therefore, the enhanced DTW algorithm is used to solve the errors caused by note segmentation.Sampling operation is performed on the audio data to obtain the sampled time domain map.The first-order difference of the speech signal increases the amplitude of the high-frequency part to balance the spectrum and alleviate the problem of tilting the range of the speech signal.According to the short-time smooth characteristics of the voice signal to the voice signal for framing processing, set the frame length for 25 ms, the towel shift for 10 ms, the movement in each towel is short-time invariant after split tilt.Use Hamming windows to multiply each frame by a sequence of windows to make the ends of each frame smoother and reduce spectral leakage.The short-time Fourier transform of the speech signal after adding windows to each frame is converted from the time domain to the frequency domain. The modulus is taken as the logarithm to generate the spectrogram.The audio semantic recognition model is the core of the audio opinion analysis system. The conversion of audio signals to text sequences is achieved by building deep neural network models. Traditional audio recognition systems use Mel-to-spectrum coefficients or linear prediction coefficients as feature vectors, which need to extract acoustic features of single-frequency signals using audio feature extraction algorithms, which are difficult to cover all valid audio information and are susceptible to noise contamination by different signal-to-noise ratios . The aud∗3, the number of channels is 32, and the step size is 2. The stride will cause the length of the time and frequency domains to be reduced to half of the input, resulting in a pooling-like effect. The second convolutional layer has a kernel size of 3∗3, a channel count of 32, and step sizes of 2 and 1, respectively. The stride will reduce the time-domain length to half of the input while the frequency domain remains unchanged. After the convolutional layer, BatchNorm2d is added to normalize the data to avoid the network instability due to a large amount of data before Hardpan's operation. 2D-CNN extracts the features and inputs the feature representation into the bidirectional GRU network for contextual feature extraction. In this paper, a 7-layer bidirectional GRU is used, and the number of neurons in the hidden layer is set to 1024 to stitch the output of both directions as the output of the GRU. The FC layer fixes the work of the GRU to the 3501-dimensional feature space. Using CTC as the loss function, the gradient of CTC loss is calculated during training, and the model parameters are adjusted by backward transfer to achieve end-to-end training of the whole model. MDCT improved discrete cosine transform is now a commonly used time-frequency transform unit in mainstream audio standard compression and one of the most computationally intensive modules, which has superiority in eliminating boundary noise and improving signal quality, and is a good IMDCT in codec algorithm; it is computationally intensive and calls high-frequency operation steps [y(n)} is the input data sequence. The following equations give MDCT and IMDCT. For social networks, it is especially important to purposefully feature mine cross-media data with special characteristics. In addition, bridging the semantic gap between media is necessary to achieve cross-media social network content search under the special data characteristics of social network multimedia content.y(k)} is the MDCT coefficient after proper windowing and time-shifting of the original sequence. Usually, it does not correspond to the original data sequence {y(n)}. An MDCT of length N is typically a power of 2. Assume that N is a multiple of 4. Replacing N-1-n by n givesThe first layer of convolution kernel size is 3on steps . Hence, n, forming the following encoding form.The improved MDCT algorithm uses N-1-n to replace the original In this era of information explosion, human beings are confronted with a massive amount of data and information every day. The traditional single-media search can no longer meet the needs of people's daily operations. Through cross-media retrieval technology, users only need to submit one media type data to get multiple media data related to its content semantics. However, the heterogeneous gap between different media data poses a significant challenge to implementing cross-media retrieval tasks. The solution idea of existing methods is shown in In the above process, the feature extraction and projection mapping of data are particularly critical. Media data has rich semantic information inside, and whether the model can effectively extract the data features will directly affect the final retrieval accuracy. In addition, the model also needs to perform cross-media association learning to complete the projection mapping process accurately . The stuFor this reason, the implementation of cross-media retrieval technology requires the support of various technologies, including computer vision, natural language processing techniques, and artificial intelligence. The remainder of this chapter describes the technical principles and knowledge related to cross-media retrieval . Cross-mConvolutional neural networks can be divided into two-dimensional convolutional neural networks (2D-CNN) and three-dimensional convolutional neural networks (3D CNN), depending on the convolution method. Different frames of gesture sequence images can be used as other input channels, and then 2D convolutional neural networks are applied. However, 2D convolutional neural networks lack the utilization of inter-frame motion information . In contThis paper is oriented to Hololens for dynamic gesture recognition. Due to the short release time of Hololens glasses, there is limited research related to them, and there is no available public gesture database, so a gesture dataset is constructed called the AMI gesture dataset . From thThe performance of Kafka and whether it can be used as consistent message architecture can be measured from the perspectives of the ability to transfer data in real-time, whether the file storage mechanism is efficiently implemented, and the effectiveness of practical applications. To improve the efficiency of message lookup in the segment file, improving the current message retrieval algorithm and to implement it can lookup messages more quickly and enhance the Kafka system performance. Since the binary lookup always divides the element from one part into two parts . The timFor audio recognition experiments, the model in this paper uses 128-dimensional log-Mel spectrograms as input features. The 1024-point Short Time Fourier Transform (STFT) is used to extract the spectrograms. It is found that a 7-second-long discourse contains enough emotional information, so if the address is longer than 7 seconds, only the middle part of the length of 7 seconds is calculated. This implementation uses Tensorflow to build the model, using Adam optimizer, with a learning rate of 0.0003 and batch size of 64. The dataset is randomly divided into ten groups, keeping the sentiment distribution constant, eight groups for training, 1 group for validation, and 1 group for testing (fixed test set). The common semantic representation between different modal data is implemented using an adversarial generative network model to achieve cross-media retrieval of scientific and technical resources by spanning the semantic divide between multimodal data. Image and text semantic sharing networks are structurally multilayer, fully connected structures. The front-layer design allows flexible setting of the number of nodes per layer of neurons according to the features of different modalities . Only thIt can be seen that in the early training period, the recall accuracy rises faster. With the increase in the number of iterations, the rate of growth in accuracy gradually decreases and finally tends to a steady trend. In the early stage of training, the model's parameters are randomly initialized and cannot fit the data distribution. With the iterations of the training process, the model parameters gradually include the data distribution. In the S&T resource dataset, we can see from the trend of loss distribution that the image feature model converges relatively fast because we use the model parameters pretrained on the large-scale image dataset. To improve the accuracy of segmentation, it is generally mixed with motion information-based and contour-based segmentation algorithms. Motion information-based gesture segmentation is mainly applied in video to do segmentation of continuously changing dynamic gestures. The gradient is continuous because the 12-parameter and cross-entropy are used as the loss function, so the model is fast. The text feature model and the cross-loss feature model also converge gradually with increasing training times. The recall rate of top5 on the training set also increased, and the growth trend slowly leveled off until more than ninety percent. To illustrate the advantages of the proposed algorithm in this paper, the mapped metric is used to measure the performance of different search methods, and the map values are calculated by the top5 and top20 search results of two tasks, text search image, and image search text, on our S&T dataset and this analysis of homo-modal and cross-modal result metrics is compared with CCA, Deep-SM, and CMDN methods. The performance comparison of the experimental results is shown in The experimental results are the average of nine cross-validations. This experiment uses three metrics, unweighted accuracy (UA), weighted accuracy (WA), and F1 score, to evaluate the model's performance. The experiments are first conducted on the IEMOCAP English dataset using different numbers of inverted residual blocks to verify the recognition effect of other residual blocks. The analysis results show that the model in this paper has a certain degree of improvement in both unweighted accuracy and weighted accuracy compared with the previously commonly used audio emotion recognition models. Compared with the 3D-ACRNN model, the unweighted accuracy of the proposed model is improved by 4.06%; compared with the DNN model, the unweighted accuracy of the proposed model is enhanced by 8.7%; compared with the BCRNN model, the unweighted accuracy of the proposed model is improved by 6.9%. In terms of the number of model parameters, 3D-ACRNN is the model with the most significant number of parameters, with 323.46 M, while the number of parameters of the DNN model is 9.5 M, which is significantly reduced compared with 3D-ACRNN; among the models in the comparison experiments, BCRNN is the model with the smallest number of parameters, with 4.34 M. Compared with the BCRNN model, which has the lowest number of parameters in the reference experiment, the number of parameters is 1.1 M, about one-fourth of the number of parameters of this model. The experiment verifies that the cross-media piano performance audio recognition and retrieval algorithm based on dynamic segmentation and comprehensive weighted matching of neural networks achieves a good recognition effect and effectively reduces the number of parameters of the model. The starting and ending frames of notes can be judged according to the amplitude drop between notes. When the threshold value is fixed, it is determined that the threshold value more significant than the threshold value indicates the start of the message and less than the threshold value indicates the cutoff of the note, which will cause three of the letters to be misjudged as one. The fundamental frequency of the region is averaged to obtain the fundamental frequency when calculating the fundamental frequency. A comparison of the piano performance audio recognition results is shown in This paper designs and implements a cross-media piano performance audio recognition and retrieval algorithm based on dynamic segmentation and weighted integrated matching of neural networks. Based on the concept of heterogeneous neural networks, this paper designs the technical solutions of the cross-media piano performance audio recognition system building module, the retrieval algorithm system data processing module, the audio recognition analysis system analysis module, and the retrieval algorithm analysis system module, respectively. Based on the design of the technical system scheme, the critical functional modules of the audio recognition analysis system and the implementation details of the algorithm logic are elaborated. Finally, the system testing process is carried out to verify the usability of the neural network-based audio recognition system. This paper focuses on the problem of matching accuracy in music retrieval algorithms, proposes an algorithm for note segmentation with a dynamic threshold to improve note segmentation accuracy, establishes a similarity matching algorithm with the weighted synthesis of basic piano templates, increases the fault tolerance mechanism of matching, and enhances the robustness and adaptability of the algorithm. This paper conducts a comprehensive analysis, comparison, and optimization of existing message retrieval algorithms. The shortcomings of current retrieval algorithms are found, and an improved message queue retrieval algorithm is designed and implemented to improve the system efficiency. The average number of lookups is significantly better than the existing message retrieval algorithm, and it can complete the message retrieval of Kafka efficiently and accurately. The performance of the improved message retrieval algorithm is also compared in terms of mathematical reasoning and the number of lookups, and the enhanced message retrieval algorithm is proved to be effective and efficient in practice.


Various constraints exist in bipedal movement. Due to the natural ability of effectively handling constraints, trajectory optimization has become one of the mainstream methods in biped gait planning, especially when constraints become much more complex on non-flat terrain. In this paper, we propose a multi-modal biped gait planner based on DIRCON, which can generate different gaits for multiple, non-flat terrains. Firstly, a virtual knot is designed to model the state transitions when the swing foot contacts terrain and is inserted as the first knot of the target trajectory of the current support phase. Thus, a complete gait or multi-modal gaits sequence can be generated at one time. Then, slacked complementary constraints, which can avoid undesired trajectories, are elaborated to describe the coupling relationships between terrain information and bipedal motion for trajectory optimization based gait planning. The concrete form of the gait planner is also delivered. Finally, we verify the performance of the planner, as well as the structural design of our newly designed biped robot in CoppeliaSim through flat terrain walking, stairs terrain walking and quincuncial piles walking. The three experiments show that the gaits planned by the proposed planner can enable the robot to walk stably over non-flat terrains, even through simple PD control. Robots are subjected to various constraints during the movement process, such as joint limits, obstacle avoidance and external contact constraints. Because the base of a biped robot floats while the supporting foot must maintain contact with the terrain and not slide, more constraints should be considered in the biped gait planning. Especially when the terrain is non-flat, such as stairs or quincuncial piles, the constraints become more stringent.Currently, Hybrid Zero Dynamics (HZD) ,2,3, ReiHowever, as the non-flat terrain further limits robot movement and the requirement of balance maintenance becomes much more difficult, more motion details and constraints need to be considered during biped gait planning to acquire a dynamic, feasible trajectory. Up to now, biped gait planning of non-flat terrain is still an open problem. Li et al.  proposedDuring biped robot movement on non-flat terrain, a coupling relationship exists between the swing foot and terrain such that the height of the swing foot has to be greater than the terrain to avoid collision when the foot reaches the position of the terrain. Moreover, some complex constraints which are difficult to express in linear form can be written in complementary form ,20,21, sThis paper is organized as follows. In The robot is a hybrid dynamic system whose state vector typically contains a general position vector The biped robot is not only a high-dimensional hybrid dynamic system but also has a constrained floating base. Because the robot needs contact with the terrain or the external environment during movement, contact force should be considered in the dynamic model. Then the dynamics can be further written as:TO can find a feasible joint trajectory because it is naturally capable of dealing with constraints, particularly in high-dimensional and constrained state space. The DC method is currently widely used in biped gait planning. Through the Simpson integration property, DC can insert the dynamics as a collocation constraint at the collocation point, which is usually located at the midpoint between two adjacent knots. The cubic Hermite spline and the first-order curve are utilized to interpolate the state trajectory and the input trajectory, respectively. Thus, DC can avoid the numerical integration process in the Shooting Method ,23 and t of tc,k , which iIn order to make the planned trajectories conform to the system dynamic, DC requires the state variables  or a sequence of multi-modal gaits (combination of gaits with different speeds), the discrete transitions should be explicitly introduced into the planner. When the contact occurs, the gait planning can be divided into two adjacent modes and the initial state of current mode is related to the last state of the previous mode, i.e., the position is maintained and the velocity abruptly changes, which complies with the impact law during state transition. We use a momentum observer  to modelFor brevity, assuming the target gait consists of two modes, left support phase (LSP) and right support phase (RSP), and the robot is switching from LSP to RSP. The state transitions can be estimated using momentum observer in the following way:The virtual knot establishes the relationship between two modes when contact happens, thus, the planner can plan the independent trajectory for each mode. The following are the main steps of the DIRCON with virtual knots:Step 1: Determine the mode sequence according to the contact condition and the robot state. Then establish optimization program based on DIRCON as Equation (5). Turn to Step 2 for virtual knots insertion;Step 2: Check if current mode happens with contact, if not, stay at Step 2 for next step, or add optimization decision variables Step 3: Insert the virtual knots as the initial knot of Step 4: Solve the optimization problem, and restore the joint level trajectories and the input trajectories by interpolation using cubic Hermite spline and first-order curve, respectively.The location of the supporting foot and the obstacle avoidance of the swing foot are two critical keys during biped motion on non-flat terrain and we will illustrate how the proposed constraints introduce them into the planner, respectively. For brevity, a four-step movement of a seven-link XOZ planar biped robot is used as an example. The selection of foot location is depicted in Assuming the selection of After foot location selection satisfies the safety requirement, we need to design a constraint to keep the swing foot from colliding with the terrain or obstacles. The swing foot obstacle avoidance is more complicated than foot location selection and the coupling relationship can be divided into three situations according to the height of target terrain and current supporting terrain: swing to flat terrain, swing to higher terrain, and swing to lower terrain. As shown in Swing to higher terrainWhen the robot swings its foot to a terrain higher than the current supporting terrain, the front and rear edges which reach the position of the terrain must be higher than the terrain height to avoid collision. Three typical trajectories that satisfy the requirement of collision avoidance of higher terrain are shown in Directly utilizing the complementary form, indeed, can guarantee the swing foot passes through the obstacle, but it cannot avoid or reduce the occurrence of the black and gray trajectories. Setting a reasonable cost function of joint input to avoid a huge acceleration is a possible method, however, the value of weight can only be determined through ample real machine experiments, and the process has to keep repeating when any parameter or terrain is changed. In order to induce the solver to converge to the green trajectory, we introduce additional slack variables and reform the complementary constraint of the front edge as follows:As shown in b.Swing to lower and flat terrainThe situation changes when the height of target terrain is lower than the supporting terrain. As shown in The purpose of the slack variable When the height of the target terrain is equal to the supporting terrain, the requirement becomes quite simple; the Z coordinate of the swing foot is greater than the support terrain at all knots, except the first and the last, and the gait should be similar to the flat terrain gait. However, in order to keep the consistency of the narrative, we still give the form of the slacked complementary constraint:The Now we present the complete form of the proposed DIRCON-based non-flat terrain gait planner. As an example, the robot would take three modes to step over two identical stairs and the robot motion is similar to In this section, the proposed planner as shown in Equation (14) is verified through three planar walking simulations: flat terrain walking, stairs terrain walking and quincuncial piles walking.The robot model used for the simulation is a newly designed biped robot by our laboratory which consists of five parts: base, hip, thigh, shank and foot. Each leg has five active joints, three for the hip, one for the knee and one for the ankle. The connector of joint is made of aluminum alloy, and the main body is made of high-strength carbon fiber. As shown in Due to the details of implementation, our hardware used to solve the optimization is “Intel Core i7 9750H” and the software environment is Ubuntu 18.04. C++ and CMake are used as implementation language and compilation tool, respectively. Coppeliasim 4.2.0  is used The first experiment is that the robot walks on flat terrain along the X direction at a speed of 0.5 m/s. At this time, the planner disables the foot location selection constraint and the swing foot constraint. The target gait is designed as a three mode sequence (LSP-RSP-LSP) which consists of 38 knots and 2 virtual knots. The first mode has 10 knots, and 20 knots are in the second. The total duration is constrained to less than 1.0 s. The second LSP also has 10 knots due to the symmetry. Furthermore, a symmetry constraint of the supporting foot location is inserted to ensure that the left support phase and right support phase take the same step length. The quality of the prior trajectory is one of the major factors influencing the performance of planner. We use a normalized random prior, and the SNOPT solving time ranges from 120 s to 3 min. Walking on stairs is the second experiment. Assume we have obtained the information of stair terrain through some other technical methods that the width and height are 3 cm and 22 cm, respectively. In contrast to walking on flat ground, the planner enables the supporting foot location constraints and swing foot constraints. We also modified the constraint on the height of the robot COM, as well as kinematic position constraints. That is, the height of the robot COM rises 6 cm in Z direction for each step and the supporting foot raises 3 cm for each contact, while the gait speed in X direction remains 0.5 m/s. Other constraints are consistent with the experiment on flat ground. The average time of solving the entire optimization is about 5.5 min. The quincuncial piles gait planning is more complicated as the constraints become much more strict, due to managing the narrow supporting area, and ensuring the swing foot does not collide with the terrain at same time. This experiment validates not only the effectiveness of the foot location selection constraint and swing foot constraint in a strict manner, but also the ability to avoid non-ideal trajectory. The distance between two piles is 11 cm, and each pile is 25 cm in length and 14 cm in width. At this point, the planner must adjust the corresponding parameters of the foot location selection constraint and the swing foot constraint based on the terrain information. In addition, we release the gait speed requirement, so that the forward speed in the X direction is just constrained to greater than or equal to 0.5 m/s. After around 5 min of solving, the solver successfully found a trajectory with a speed of 0.59 m/s. As evidenced by the three dynamic simulation experiments in