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

Salvia miltiorrhiza injection ameliorates myocardial ischemia-reperfusion injury via downregulation of PECAM-1

Purpose: To investigate the effect of Salvia miltiorrhiza injection on myocardial ischemia-reperfusion injury and PECAM-1 related pathways. Method: Male Wistar rats were used for establishment of myocardial ischemia-reperfusion model. The rats were randomly assigned to four groups: experimental group, low dose group (Salvia miltiorrhiza injection, 10 mL/kg/day), moderate dose group (Salvia miltiorrhiza injection, 20 mL/kg/day) and high dose group (Salvia miltiorrhiza injection, 40 mL/kg/day). Myocardial ischemia-reperfusion model was established in the four groups. Evans-TTC staining was used to assess relative area of ischemiareperfusion injury. Blood samples were collected for assay of PECAM-1 expression using enzymelinked immunosorbent assay (ELISA). Fresh blood platelets were collected in all groups, and divided into two groups - control group (normal culture) and experimental group (Salvia miltiorrhiza injection). The expression of PECAM-1 in blood platelets was assayed using Western blot. Result: Compared with the experimental group, Salvia miltiorrhiza injection ameliorated myocardial ischemia-reperfusion injury, and decreased the infarction area seen in Evans/TTC staining. PECAM-1 expression in blood was decreased by Salvia miltiorrhiza injection. Blood platelets dysfunction was induced after myocardial ischemia-reperfusion, and the level of PECAM-1 increased. However, Salvia miltiorrhiza injection treatment downregulated the expression of PECAM-1 after myocardial ischemiareperfusion. Conclusion: Salvia miltiorrhiza injection maintains normal function of blood platelets and ameliorates myocardial ischemia-reperfusion injury by decreasing expression of PECAM-1

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers

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

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

Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via activation of p38MAPK and inhibition of NF-κB signaling pathway

Purpose: To investigate the anti-proliferative and apoptotic influences of alantolactone on gastric carcinoma (GC) cell lines, and the mechanism(s) involved. Methods: Human gastric cancer cell line (BGC823) and gastric adenocarcinoma lymph node metastasis cell line (SGC7901) were maintained in Ham’s F12 medium supplemented with 10 % heatinactivated fetal bovine serum (FBS). In each group of cancer cell line, 5 groups of cells were used: control and four alantolactone groups which were treated with increasing concentrations of alantolactone (5 - 30 μM) for varying periods. Proliferation was determined using MTT assay, while realtime quantitative polymerase chain reaction (qRT-PCR) was used to assay the expressions of apoptosis- and metastasis-related genes. The expressions of p38MAPK and nuclear transcription factor-κB (NF-κB) in BGC823 and SGC7901 cells were measured with Western blotting. Results: Phosphorylated protein (p-p38 protein) expression was significantly higher in both groups of GC cells, relative to control (p < 0.05). The expressions of NF-κB in plasma protein were markedly higher in both groups of GC cells than in control group, but the corresponding expressions in nuclear protein were significantly lower in both groups of GC cells, relative to control (p < 0.05). Conclusion: Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via mechanisms involving activation of the p38MAPK, and inhibition of the NF-κB signaling pathways. Thus, alantolactone may be a new and effective anti-gastric cancer drug