Online Synthesis Research Articles

Data-efficient safe learning and control with on-board sensors: Bayesian meta-learning and barrier function based approach

We consider the safe navigation of robots in unknown environments using data from sensory devices. The control barrier function (CBF) is one of the promising approaches to enforcing safety in robot navigation and the recent progress on learning-based approaches realizes online synthesis of CBF-based safe controllers with sensor measurements. However, the learned CBF candidates in these works cannot be generalized to different environments and the re-synthesis is necessary when changes in the environment occur. With this observation, this paper attempts to develop a method that can effectively use past data in different environments to quickly learn the CBF candidate in the current setting by leveraging the currently developed Bayesian meta-learning framework. Our method realizes data-efficient online learning as shown in the simulation and provides safety guarantees on the resulting controller.

Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS

Brain–computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant’s voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.

Unveiling the Dynamic Electrocatalytic Activity of Online Synthesized Bimetallic Nanocatalysts via Electrochemiluminescence Microscopy.

Effective bimetallic nanoelectrocatalysis demands precise control of composition, structure, and understanding catalytic mechanisms. To address these challenges, we employ a two-in-one approach, integrating online synthesis with real-time imaging of bimetallic Au@Metal core-shell nanoparticles (Au@M NPs) via electrochemiluminescence microscopy (ECLM). Within 120 s, online electrodeposition and in situ catalytic activity screening alternate. ECLM captures transient faradaic processes during potential switches, visualizes electrochemical processes in real-time, and tracks catalytic activity dynamics at the single-particle level. Analysis using ECL photon flux density eliminates size effects and yields quantitative electrocatalytic activity results. Notably, a nonlinear activity trend corresponding to the shell metal to Au surface atomic ratio is discerned, quantifying the optimal surface component ratio of Au@M NPs. This approach offers a comprehensive understanding of catalytic behavior during the deposition process with high spatiotemporal resolution, which is crucial for tailoring efficient bimetallic nanocatalysts for diverse applications.

Silver Nanoparticles for Ultrasonic Assisted Synthesis of Oxidant Agents in Micro-Reactor: Kinetic Analysis and Process Intensification

Chemical engineering frequently uses "process intensification" to consciously combine various phenomena or procedures. By treating the molecules in such a system in a way that every single molecule experiences the same processing, the selectivity is raised, enhancing productivity. For mass transfer limited reactions, the enhancement of the transport rates & the specific interfacial area are the typical approaches. These enable the reduction of diffusion path length, reduce hold-up and improve the controlling on temperature control, even for highly exothermic reactions. Micro reactor technology (MRT) is a subset of process intensification that aims to reduce the size of equipment, energy consumption, and waste generation. The research of peracetic acid (PAA) and perform acid (PFA) preparation is the focus of the current investigation. Amberlite IR-120H catalyst was used to study the synthesis of PAA and PFA in batch and micro-structured reactors while ultrasonic irradiations were present.. The current research describes a method for synthesizing both compounds in a batch and micro-structured reactors, with and without ultrasonic irradiation. Such a technology might be crucial in the online synthesis of these chemicals as it eliminates the need for harmful components to be transported and stored, assuring safety among other benefits. For these substances, various safety characteristics could be improved.

Priority-based state machine synthesis that relaxes behavior design of multi-arm manipulators in dynamic environments

This paper presents a framework governing multiple state machines (SMs) to generate behaviors for kinematically-coupled multi-arm robots. A SM is one of the most used tools to switch controllers in robotics, however, it represents only one motion at a certain time. Such a limit of representation not only restricts the potential of the kinematical redundancy but also significantly increases the number of situations we have to consider in behavior design. To relax the problem, this paper provides a distributed design scheme and an online synthesis method for SMs. The base of the framework is a task-priority-based control method for redundant robots. We expand it by introducing an abstract data structure specialized in robotic behaviors. The abstract reformulation naturally connects with SMs so that the framework realizes parallelism of sequential logic. A priority queue constructed in each control loop integrates SMs designed for each effector, and the framework automatically generates whole-body behaviors online. In dynamic simulations, the framework governs four SMs and achieves reaching tasks with a dual-arm manipulator at 1 kHz. It is shown that the loose coupling of the SMs yields reactive and flexible decisions in a dynamic environment.

On-line carbon dots synthesis using flow injection analysis. Application to aluminium determination in water samples

An on-line synthesis of CDots is proposed for the first time, using the flow injection analysis (FIA) technique, which was coupled, in a single system, to the analytical determination of aluminium in water samples. The nanoparticles were obtained from the carbonization of glucose and iron(III) in an acidic medium, and their photoluminescence increased in the presence of aluminium ions. Under optimal experimental conditions, the proposed method has shown an acceptable linearity range –between 0.04 and 3.0 mg L−1 (R2 = 0.9999) – and a detection limit of 0.007 mg L−1. The analysis of drinking water and groundwater samples showed good accuracy (recoveries ranged between 91 – 113%) and RSD% < 13. The on-line system exhibited a high sample throughput (36 h−1), since no incubation time was required.

Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges

The state of charge (SOC) and the state of health (SOH) are two crucial parameters for monitoring the battery status because SOH determines whether the battery can continue to operate safely and stably, and the SOC determines the battery's endurance. This paper proposes an online synthesis method based on the response characteristics of load surges and an improved fuzzy cerebellar model neural network (IFCMNN) to co-estimate SOH and SOC. Firstly, multiple features are extracted from the voltage response signals. Then, the grey relational analysis is utilized to verify the rationality of the extracted features and to fuse key features for SOH and SOC estimation. Secondly, IFCMNN models aiming at estimating SOH and SOC are proposed to estimate the SOH and SOC simultaneously and quickly. Finally, experimental results on ten batteries with different aging levels show that the proposed method can achieve fast estimation of SOH and SOC at 1.64 % and 2 % resolution accuracy respectively, regardless of the temperature and in rush currents variations. In addition, the proposed model has higher estimation accuracy compared with other traditional methods. Thus, the proposed method has a high generalization ability.

Co-Design for Enhancing Flood Resilience in Davao City, Philippines

Enhancing flood resilience, including the development of social capacity and early warning systems, in addition to structural measures, is one of the key solutions to mitigating flood damage, which will be more intensified in the future due to climate change. This study was conducted to develop a comprehensive methodology for enhancing flood resilience by improving society-wide disaster literacy under the governance formed through the active participation of all levels of stakeholders in Davao City, Philippines. Specifically, the development of the Online Synthesis System for Sustainability and Resilience, which integrates different disciplines, and the fostering of Facilitators, whose role is to interlink the science community and society, were implemented in a co-designing manner by the collective governance body. The development of basin- and barangay-scale hydrological models realized real-time flood forecasting and climate change impact assessment to identify intensified flood risk under the future climate. Co-designed e-learning workshops were held to foster about thirty Facilitators and help them produce twenty-one risk communication plans and workshop designs for fourteen barangays considering geographic, demographic, economic, and social features that they can utilize for public dissemination related to climate change adaptation to the target audiences in society. This paper presents a practical method to enhance flood resilience, demonstrating that the synthesis of science-based knowledge and human resource development can fill the gaps between the science community and society.

Gaussian Control Barrier Functions: Non-Parametric Paradigm to Safety

Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise of data-driven techniques for modeling functions, we propose a non-parametric approach for online synthesis of CBFs using Gaussian Processes (GPs). A dynamical system is defined to be safe if a subset of its states remains within the prescribed set, also called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe set</i> . CBFs achieve safety by designing a candidate function a priori. However, designing such a function can be challenging. Consider designing a CBF in a disaster recovery scenario where safe and navigable regions need to be determined. The decision boundary for safety here is unknown and cannot be designed a priori. Moreover, CBFs employ a parametric design approach and cannot handle arbitrary changes to the safe set in practice. In our approach, we work with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety samples</i> to construct the CBF online by assuming a flexible GP prior on these samples, and term our formulation as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gaussian</i> CBF. GPs have favorable properties such as analytical tractability and robust uncertainty estimation. This allows realizing the posterior with high safety guarantees while also computing associated partial derivatives analytically for safe control. Moreover, Gaussian CBFs can change the safe set arbitrarily based on sampled data, thus allowing non-convex safe sets. We validated experimentally on a quadrotor by demonstrating safe control for 1) arbitrary safe sets, 2) collision avoidance with online safe set synthesis, 3) and juxtaposed Gaussian CBFs with CBFs in the presence of noisy states. The experiment video link is: https://youtu.be/HX6uokvCiGk.

Digital Mega-Studies as a New Research Paradigm: Governing the Health Research of the Future.

'Digital Mega-Studies' are entirely or extensively digitised, longitudinal, population-scale initiatives, collecting, storing, and making available individual-level research data of different types and from multiple sources, shaped by technological developments and unforeseeable risks over time. The Australian 'Gen V' project exemplifies this new research paradigm. In 2019, we undertook a multidisciplinary, multi-stakeholder process to map Digital Mega-Studies' key characteristics, legal and governance challenges and likely solutions. We conducted large and small group processes within a one-day symposium and directed online synthesis and group prioritisation over subsequent weeks. We present our methods (including elicitation, affinity mapping and prioritisation processes) and findings, proposing six priority governance principles across three areas-data, participation, trust-to support future high-quality, large-scale digital research in health.

Microwave-assisted continuous flow phytosynthesis of silver nanoparticle/reduced graphene oxide composites and related visible light catalytic performance

The creation of an environmentally friendly synthesis method for silver nanomaterials (AgNPs) is an urgent concern for sustainable nanotechnology development. In the present study, a novel straightforward and green method for the preparation of silver nanoparticle/reduced graphene oxide (AgNP/rGO) composites was successfully developed through the combination of phytosynthesis, continuous flow synthesis and microwave-assistance. Oriental persimmon (Diospyros kaki Thunb.) extracts were used as both plant reducing and capping agents for fast online synthesis of AgNP/rGO composites. The experimental parameters were optimized and the morphologies of the prepared materials were investigated. The characterization results reveal that spherical AgNPs were quickly synthesized and uniformly dispersed on rGO sheets using the proposed online system. Fourier transform infrared spectroscopy analysis confirmed that phenols, flavonoids, and other substances in the plant extracts played a decisive role in the synthesis of AgNP/rGO composites. Using sodium borohydride (NaBH4) degradation of p-nitrophenol (4-NP) as a model, the catalytic activity of the prepared AgNP/rGO materials was evaluated. The complete degradation of 4-NP was achieved within 12 min through the use of AgNP/rGO materials, and the composite had a much better catalytic activity than the bare AgNPs and rGO had. Compared with the conventional chemical method, our online method is facile, fast, cost-efficient, and environmentally friendly.

Online Synthesis for Runtime Enforcement of Safety in Multiagent Systems

We study the problem of enforcing safety in multiagent systems at runtime by modifying the system behavior if a potential safety violation is detected. Traditional runtime enforcement methods that solve a reactive synthesis problem at design time have two significant drawbacks. First, these techniques do not scale as one has to take into account all possible behaviors from every agent, and this is computationally prohibitive. Second, these approaches require every agent to know the state of every other agent. We address these limitations through a new approach where online modifications to behavior are synthesized onboard every agent. There is an enforcer onboard every agent, which can modify the behavior of only the corresponding agent. In this approach, which is naturally decentralized, the enforcer on every agent has two components: 1) a pathfinder that corrects the behavior of the agent and 2) an ordering mechanism that dynamically modifies the priority of the agent. The current priority of an agent determines if the enforcer uses the pathfinder to modify the behavior of the agent. We derive an upper bound on the maximum deviation for any agent from its original behavior; that is, all agents make progress. We prove that the worst case synthesis time is quadratic in the number of agents at runtime as opposed to exponential at design time for the existing methods that rely on design-time computation merely. Additionally, we prove the completeness of the technique under some mild assumptions; that is, if the agents can progress safely, then enforcers will find this behavior. We test the technique in collision avoidance scenarios. For 50 agents in a 50 × 50 grid world modeling the common workspace for the agents, the online synthesis requires only a few seconds per agent whenever a potential collision is detected. In contrast, the centralized design-time synthesis of shields for a similar setting is intractable beyond four agents in a 5 × 5 grid world.

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) reference trajectory. The paper deals with the online synthesis of the optimal SoC reference trajectory, which inherently includes adaptive features in relation to the prediction of upcoming driving cycle features such as the trip distance, the road grade profile, the mean vehicle velocity and the mean demanded power. The method performs iteratively, starting from an offline-synthesized SoC reference trajectory obtained based on dynamic programming (DP) control variable optimization results. The overall PHEV control strategy incorporating the proposed online SoC reference trajectory synthesis method is verified against the DP benchmark and different offline synthesis methods. For this purpose, a model of a PHEV-type city bus is used and simulated over a wide range of driving cycles and conditions including varying road grade and low-emission zones (LEZ).

DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes; and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples.

Analysis and Design of Drivetrain Control for the AEV With Network-Induced Compounding-Construction Loop Delays

This paper presents a robust predictive control scheme with a graphic-based delay boundary analysis to mitigate the electric vehicle (EV) drivetrain oscillating issues, subject to the multi-channel compounding-construction loop delays. The application of Controller Area Network (CAN) in autonomous electric vehicles (AEVs) inevitably induces multi-channel compounding-construction loop delays into the control loop. The in-deep analyzing and understanding of the network-induced loop delays is critical for the electrified powertrain and its motion control. This study aims to guarantee, explicitly, the motion stability of AEV drivetrains as safe-critical and hard real-time applications. Firstly, a graphic-based constructional representation approach is presented for modeling of the compounding-construction loop delays. To resolve the upper bound of the compounding-construction loop delays further, a mathematic expression of delay boundary-envelopment analysis is derived. Secondly, based on the reasonable upper bound, Taylor series expansion is applied to make the system model with nonlinear uncertainties caused by the network-induced loop delays represent in the form of the convex polytope. Then, with the convex polytope of the drivetrain system model, a robust model predictive control (RMPC) approach is developed to enhance the system robustness against the unexpected network-induced delays. To attenuate the online calculation burden, a scheme combining off-line design and on-line synthesis is provided. Finally, the satisfactory motion control performance in both the co-simulations (Matlab&Carsim) and bench experimental tests can strongly verify the effectiveness of the proposed approaches.

Integration of 3D Hydrodynamic Focused Microreactor with Microfluidic Chemiluminescence Sensing for Online Synthesis and Catalytical Characterization of Gold Nanoparticles.

Chemiluminescence assays have shown great advantages compared with other optical techniques. Gold nanoparticles have drawn much attention in chemiluminescence analysis systems as an enzyme-free catalyst. The catalytic activity of gold nanoparticles for chemiluminescence sensing depends on size, shape and the surface charge property, which is hard to characterize in batches. As there is no positive or negative correlation between chemiluminescence signals and sizes of gold nanoparticles, the best way to get optimal gold nanoparticles is to control the reaction conditions via online chemiluminescence sensing systems. Therefore, a new method was developed for online synthesis of gold nanoparticles with a three-dimension hydrodynamic focusing microreactor, directly coupled with a microfluidic chemiluminescence sensing chip, which was coupled to a charge-coupled device camera for direct catalytical characterization of gold nanoparticles. All operations were performed in an automatic way with a program controlled by Matlab. Gold nanoparticles were synthesized through a single-phase reaction using glucose as a reducing agent and stabilizer at room temperature. The property of gold nanoparticles was easily controlled with the three-dimension microreactor during synthesis. The catalyst property of synthesized gold nanoparticles was characterized in a luminol–NaOCl chemiluminescence system. After optimizing parameters of synthesis, the chemiluminescence signal was enhanced to a factor of 171. The gold nanoparticles synthesized under optimal conditions for the luminol–NaOCl system were stable for at least one month. To further investigate the catalytic activity of synthesized gold nanoparticles in various situations, two methods were used to change the property of gold nanoparticles. After adding a certain amount of salt (NaCl), gold nanoparticles aggregated with a changed surface charge property and the catalytic activity was greatly enhanced. Glutathione was used as an example of molecules with thiol groups which interact with gold nanoparticles and reduce the catalytic activity. The chemiluminescence intensity was reduced by 98.9%. Therefore, we could show that using a microreactor for gold nanoparticles synthesis and direct coupling with microfluidic chemiluminescence sensing offers a promising monitoring method to find the best synthesis condition of gold nanoparticles for catalytic activity.

On-line synthesis of parsers for string events

A string event is the occurrence of a specific pattern in the textual output of a program. The capture and treatment of string events has several applications, such as log anonymization, error handling and user notification. However, there is no systematic approach to identify and treat string events today. This paper formally defines string events and brings forward the theory and practice of a general framework to handle them. The framework encompasses an example-based user interface to specify string patterns plus a grammar synthesizer that allows efficiently parsing such patterns. We demonstrate the effectiveness of this framework by using it to implement Zhefuscator , a system that redacts occurrences of sensitive information in database logs. Zhefuscator is implemented as an extension to the Java Virtual Machine (JVM). It intercepts patterns of interest on-the-fly and does not require interventions in the source code of the protected program. It can infer log formats and capture string events with minimal performance overhead. As an illustration, it is up to 14x faster than an equivalent brute-force approach, converging to a definitive grammar after observing less than 10 examples from typical logs.

Preparation of cetyl palmitate-based PEGylated solid lipid nanoparticles by microfluidic technique

In recent years, several studies have shown that the use of solid lipid nanoparticles (SLN) as a colloidal drug delivery system was more advantageous than lipid emulsions, liposomes and polymeric nanoparticles. SLNs have numerous advantages of different nanosystems and rule out many of their drawbacks. Despite the numerous advantages of SLNs, translation from the preclinical formulation to the industrial scale-up is limited. In order to provide a reproducible and reliable method of producing nanoparticles, and thus, obtain an industrial scale-up, several methods of synthesis of nanoparticles by microfluidic have been developed. Microfluidic technique allows a good control and a continuous online synthesis of nanosystems compared to synthesis in bulk, leading to a narrow size distribution, high batch-to-batch reproducibility, as well as to the industrial scale-up feasibility. This work described the optimization process to produce SLNs by microfluidics. The SLNs produced by microfluidics were characterized by complementary optical and morphological techniques and compared with those produced by bulk method. SLNs were loaded with paclitaxel and sorafenib, used as model drugs. The anti-cancer efficiency of the SLNs formulation was estimated with 2D and 3D tumour models of two different cell lines, and the cellular uptake was also studied with fluorescence-assisted measurements.

On-Line Synthesis of Permissive Supervisors for Partially Observed Discrete Event Systems under scLTL Constraints

Abstract We consider a supervisory control problem of a discrete event system (DES) under partial observation, where a control specification is given by a fragment of linear temporal logic We design an on-line supervisor that dynamically computes its control action with the complete information of the product automaton of the DES and an acceptor for the specification. The concepts of controllability and observability are defined by means of a ranking function defined on the product automaton, which decreases its value if an accepting state of the produc automaton is being approached. The proposed on-line control scheme leverages the ranking function and a permissiveness function, which represents a time-varying permissiveness level As a result, the on-line supervisor achieves the specification, being aware of the tradeoff between its permissiveness and acceptance of the specification, if the product automaton is controllable and observable.

Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

Abstract The state of health (SOH) and remaining useful lifetime (RUL) estimation are important parameters for battery health forecasting as they reflect the health condition of battery and provide a basis for battery replacement. This study proposes a novel on-line synthesis method based on the fusion of partial incremental capacity and artificial neural network (ANN) to estimate SOH and RUL under constant current discharge. Firstly, the advanced filter methods are applied to smooth the initial incremental capacity curves. Then the strong correlation feature values are extracted from the partial incremental curves by using correlation analysis methods. Finally, two ANN models aiming at estimating SOH and RUL are established to estimate the SOH and RUL simultaneously. The training and verification results indicate that the proposed method has highly reliability and accuracy for SOH and RUL estimation.