Synthesis strategies to 3-alkyl-2-methoxy pyrazines

Pyrazines are a class of heterocyclic compounds with multiple industrial applications spanning the flavours, fragrance, and pharmaceutical industries. The investigation of new synthetic routes leading to improved safety, efficiency and cost are advantageous to large scale synthesis in each of these sectors. One of our laboratories principle ambitions is to establish generic methodology that allows access to a wide range of novel heterocyclic cores such as pyrazines. To develop this chemistry, we selected three exemplifying structures namely, 2-butyl-3-methoxypyrazine, 2-propyl-3-methoxypyrazine, and 2-isobutyl-3-methoxypyrazine, with the eventual aim of enabling large scale production. Prior to describing the synthesis of these compounds, a background literature review of current pyrazine preparation is provided. This includes the unique odour characters of this family, their application in the flavour and fragrance area and biological impacts as semiochemicals. A more comprehensive pyrazine natural formation also provided some insights and data to estimate the reaction conditions and starting materials required for the new synthesis. The retrosynthesis of 2-alkoxy-3-methoxypyrazines is shown in the abstract figure. Firstly, the determination of key variables relating to yield based upon a known synthetic route suggested by Seifert et al. in 1970, was investigated.[1] Further literature sources were also reviewed to identify improvements for each step of the originally established scheme. Overall, around 50% yield of the selected target 2-isobutyl-3-methoxypyraizne were obtained and under milder reaction conditions. Secondly, a new route was considered for the synthesis of 2-isobutyl-3-methxoypyrazine via condensation of α,β-dicarbonyl and of α,β-diamine compounds. The method was based upon an approach by Ghosh et al. published in 2011.[2] The scope of this route was analysed and was adjusted to enable us to prepare 2-isobutyl-3-methoxypyrazines via a novel aromatisation procedure

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

An investigation into reducing the spindle acceleration energy consumption of machine tools

Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Learning Sequence Descriptor based on Spatiotemporal Attention for Visual Place Recognition

Sequence-based visual place recognition (sVPR) aims to match frame sequences with frames stored in a reference map for localization. Existing methods include sequence matching and sequence descriptor-based retrieval. The former is based on the assumption of constant velocity, which is difficult to hold in real scenarios and does not get rid of the intrinsic single frame descriptor mismatch. The latter solves this problem by extracting a descriptor for the whole sequence, but current sequence descriptors are only constructed by feature aggregation of multi-frames, with no temporal information interaction. In this paper, we propose a sequential descriptor extraction method to fuse spatiotemporal information effectively and generate discriminative descriptors. Specifically, similar features on the same frame focu on each other and learn space structure, and the same local regions of different frames learn local feature changes over time. And we use sliding windows to control the temporal self-attention range and adpot relative position encoding to construct the positional relationships between different features, which allows our descriptor to capture the inherent dynamics in the frame sequence and local feature motion

Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology