An experimental study on the effects of spring stiffness on the combustion and dynamic characteristics of a linear engine

We investigated the effects of the stiffness of springs applied to linear compressor chambers experimentally. The applied springs could prevent conflict between the engine piston head and engine head cover as well as be available to save the regenerative energy. The linear engine bore size was 30 mm, the maximum stroke was 31 mm and effective stroke volume of a cylinder was 25.45 cc. The spring stiffness was varied: 0, 0.5, 1.00, 2.9 and 14.7 N/mm. The linear engine was fueled with a pre-mixture combined with LPG (propane 99%) and air. As an experimental result, higher spring stiffness affected growth in regenerative energy and stroke. Also, IMEP was increased by rising stroke. Finally, increased IMEP and regenerative energy made more electricity than no spring.     

A simple scheme with coupled down converters with type-I phase matching as resource for conditional preparation of macroscopic entangled states

It is shown that macroscopic entangled states can be generated using an experimental arrangement consisting of coupled spontaneous parametric down-converters with type-I phase matching (SPDCI) pumped simultaneously by optical fields in coherent state and two beam splitters. Two beam splitters in auxiliary generated modes are used to conditionally prepare macroscopic entangled states in output pumping modes of the studied system. Identification of two macroscopic entangled states is produced by use of photon number resolving detection. In contrast to all previous schemes, our scheme does not need Kerr-type nonlinear interaction and is purely based on second-order susceptibility of the crystal which is stronger for the Kerr nonlinearity. We calculate concurrence of the states as a measure of the amount of entanglement stored in the states and present analysis concerning ‘separation’ between components forming studied entangled states.     

Imparting Metal Oxides with High Sensitivity Toward Light-Activated NO2 Detection Via Tailored Interfacial Chemistry

Activation of metal oxides by light is a robust yet facile approach to manipulating their surface chemistry for favorable reactions with target molecules in heterogeneous catalysis and gas sensors. However, a limited understanding of interface chemistry and the involved mechanism impedes the development of a rational design of oxide interfaces for light-activated gas sensing. Herein, the TiO_x-assisted photosensitization of In₂O₃ toward NO₂ sensing is investigated as a case study to elucidate the detailed mechanism of light-activated surface chemistry at the metal/gas interface. The resultant heterogeneous oxides exhibit outstanding NO₂ sensing performance under light irradiation thanks to abundant photoexcited electrons and holes that serve as adsorption and desorption sites, respectively, to accelerate both surface reactions. Furthermore, the facile transfer of electrons and holes across the TiO_x-In₂O₃ interface contributes to improving the reversibility of sensing kinetics. Through this study, the mechanistic understanding is established of how the surface chemistry of metal oxide surfaces can be tuned by light activation providing an effective route to the design fabrication of high-performance gas sensors.     

Electronic Skin: Recent Progress and Future Prospects for Skin‐Attachable Devices for Health Monitoring,Robotics, and Prosthetics

Recent progress in electronic skin or e‐skin research is broadly reviewed, focusing on technologies needed in three main applications: skin‐attachable electronics, robotics, and prosthetics. First, since e‐skin will be exposed to prolonged stresses of various kinds and needs to be conformally adhered to irregularly shaped surfaces, materials with intrinsic stretchability and self‐healing properties are of great importance. Second, tactile sensing capability such as the detection of pressure, strain, slip, force vector, and temperature are important for health monitoring in skin attachable devices, and to enable object manipulation and detection of surrounding environment for robotics and prosthetics. For skin attachable devices, chemical and electrophysiological sensing and wireless signal communication are of high significance to fully gauge the state of health of users and to ensure user comfort. For robotics and prosthetics, large‐area integration on 3D surfaces in a facile and scalable manner is critical. Furthermore, new signal processing strategies using neuromorphic devices are needed to efficiently process tactile information in a parallel and low power manner. For prosthetics, neural interfacing electrodes are of high importance. These topics are discussed, focusing on progress, current challenges, and future prospects.     

Conjugated Carbon Cyclic Nanorings as Additives for Intrinsically Stretchable Semiconducting Polymers

Molecular additives are often used to enhance dynamic motion of polymeric chains, which subsequently alter the functional and physical properties of polymers. However, controlling the chain dynamics of semiconducting polymer thin films and understanding the fundamental mechanisms of such changes is a new area of research. Here, cycloparaphenylenes (CPPs) are used as conjugated molecular additives to tune the dynamic behaviors of diketopyrrolopyrrole‐based (DPP‐based) semiconducting polymers. It is observed that the addition of CPPs results in significant improvement in the stretchability of the DPP‐based polymers without adversely affecting their mobility, which arises from the enhanced polymer dynamic motion and reduced long‐range crystalline order. The polymer films retain their fiber‐like morphology and short‐range ordered aggregates, which leads to high mobility. Fully stretchable transistors are subsequently fabricated using CPP/semiconductor composites as active layers. These composites are observed to maintain high mobilities when strained and after repeated applied strains. Interestingly, CPPs are also observed to improve the contact resistance and charge transport of the fully stretchable transistors. ln summary, these results collectively indicate that controlling the dynamic motion of polymer semiconductors is proved to be an effective way to improve their stretchability.     

Agent-based system identification for control-oriented building models

The paper presents a general agent-based system identification framework as potential solution for data-driven models of building systems that can be developed and integrated with improved efficiency, flexibility and scalability, compared to centralized approaches. The proposed method introduces building sub-system agents, which are optimized independently, by solving locally a maximum likelihood estimation problem. Several models are considered for the sub-system agents and a systematic selection approach is established considering the root mean square error, the parameter sensitivity to output trajectory and the parameter correlation. The final model is integrated from selected models for each agent. Two different approaches are developed for the integration; the negotiated-shared parameter model, which is a distributed method, and the free-shared parameter model based on a decentralized method. The results from a case-study for a high performance building indicate that the model prediction accuracy of the new approach is fairly good for implementation in predictive control.     

A framework for discovering relevant patterns using aggregation and intelligent data mining agents in telematics systems

The emerging technology in vehicle telematics drives several stakeholders in this field to consider services that could be beneficial for both clients and the telematics service providers. In particular this paper proposes a novel framework for insurance telematics in Korea using a mobile aggregation agent (AA) and intelligent data mining agent (IDMA). To our knowledge, this model is recent of its kind in this country and the base-line information from driver’s characteristics serves as reference for the flexible insurance policies. We are able to present a use-case scenario and illustrative examples to demonstrate our model. With this flexible insurance framework, customers can manage their own insurance premiums and lower the cost of motoring. Promising applications of this system to business and industries have been recognized and discussed.     

Reinforcement learning-based dynamic obstacle avoidance and integration of path planning

Deep reinforcement learning has the advantage of being able to encode fairly complex behaviors by collecting and learning empirical information. In the current study, we have proposed a framework for reinforcement learning in decentralized collision avoidance where each agent independently makes its decision without communication with others. In an environment exposed to various kinds of dynamic obstacles with irregular movements, mobile robot agents could learn how to avoid obstacles and reach a target point efficiently. Moreover, a path planner was integrated with the reinforcement learning-based obstacle avoidance to solve the problem of not finding a path in a specific situation, thereby imposing path efficiency. The robots were trained about the policy of obstacle avoidance in environments where dynamic characteristics were considered with soft actor critic algorithm. The trained policy was implemented in the robot operating system (ROS), tested in virtual and real environments for the differential drive wheel robot to prove the effectiveness of the proposed method. Videos are available at https://youtu.be/xxzoh1XbAl0.