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Fire poses a significant risk across industrial and domestic settings, especially to firefighters who must tackle the blaze. Current technology for detection in indoor environments are smoke detectors and flame detectors. However, these detectors have several limitations during the ignition phase of a fire and propagation. These systems cannot detect an exact position of the fire nor how the fire is spreading or its size, all of which is necessary information for fire services when dealing with these incidents. A potential solution is to use artificial intelligence techniques such as computer vision, which has shown the potential to detect and recognise objects and activities in indoor spaces. This study aims to develop a vision-based fire and smoke detection system. A deep learning technique that incorporates convolutional neural networks (CNN) was utilised to develop the real-time detection approach that can potentially provide necessary information for fire services, including identifying the position and size of the fire and how the fire spreads. A transfer learning approach using a pre-trained model was used to train the detector. Based on the detection and recognition tests using indoor fire and smoke videos, results indicated that the fire detection achieved up to 92.37% correct detections while the smoke detection did not perform as well. Hence, further improvement and evaluation of the detection approach will be conducted in future work, focusing on the impact of different parameters such as the detection model, building type, indoor space size and positioning of the detection camera. The present study provides an insight into the capabilities and potential applications of the concept.

Building ventilation accounts for up to 30% of the heat loss in commercial buildings and 25% in industrial buildings. To effectively aid the reduction of energy consumption in the building sector, the development of demand-driven control systems for heating ventilation and air-conditioning (HVAC) is necessary. In countries with temperate climates such as the UK, many buildings depend on natural ventilation strategies such as openable windows, which are useful for reducing overheating prevalence during the summer. The manual opening and adjustment of windows by occupants, particularly during the heating season, can lead to substantial heat loss and consequent energy consumption. This could also result in the unnecessary or over ventilation of the space, or the fresh air is more than what is required to ensure adequate air quality. Furthermore, energy losses build up when windows are left open for extended periods. Hence, it is important to develop control strategies that can detect and recognise the period and amount of window opening in real-time and at the same time adjust the HVAC systems to minimise energy wastage and maintain indoor environment quality and thermal comfort. This paper presents a vision-based deep learning framework for the detection and recognition of manual window operation in buildings. A trained deep learning model is deployed into an artificial intelligence-powered camera. To assess the proposed strategy's capabilities, building energy simulation was used with various operation profiles of the opening of the windows based on various scenarios. Initial experimental tests were conducted within a university lecture room with a south-facing window. Deep learning influenced profile (DLIP) was generated via the framework, which uses real-time window detection and recognition data. The generated DLIP were compared with the actual observations, and the initial detection results showed that the method was capable of identifying windows that were opened and had an average accuracy of 97.29%. The results for the three scenarios showed that the proposed strategy could potentially be used to help adjust the HVAC setpoint or alert the occupants or building managers to prevent unnecessary heating demand. Further developments include enhancing the framework ability to detect multiple window opening types and sizes and the detection accuracy by optimising the model.

As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.

User demand for increased internal thermal comfort conditions have resulted in rising energy costs for space-heating consumption. The present study aims to recover the thermal energy in ventilation exhaust air and transfer the energy to the incoming air, to be redistributed using natural ventilation windcatcher. A comprehensive review was carried out to explore heat recovery systems that can potentially be incorporated with natural ventilation wind catchers. A rotary heat recovery device suitable to be incorporated with a roof mounted multi directional windcatcher system was developed. Computational Fluid Dynamics (CFD) modelling and laboratory experimental tests were conducted to investigate the proposed system. In the first phase, a full-scale prototype of the passive rotary thermal wheel device was developed and tested in a crossflow channel to initially assess the concept and performance of the design. Two configurations of the passive heat recovery wheel were tested: 20 and 32 radial blades. The second phase focused on investigating the integration of heat recovery wheel into a windcatcher system. CFD modelling and scaled wind tunnel testing were conducted to assess the airflow and temperature distribution around the multi-directional windcatcher with a passive rotary wheel. The results showed that the addition of the heat recovery wheel rotating at 15 rpm reduced the indoor airflow speed between 14 and 30%, depending on the outdoor wind conditions. The system was able to provide the recommended fresh air rates when the outdoor wind speed was 1.5 m/s and higher. In addition to sufficient ventilation, the heat recovery system had a positive impact on the indoor air temperature, raising the temperature up to 3.7 °C depending on the indoor/outdoor conditions.

This work builds on previous experience in windcatcher design, maximising the ventilation rate of the windcatcher, whilst integrating low energy cooling technologies. The present study aims to investigate the thermal comfort and indoor air quality in buildings ventilated with a passive cooling windcatcher integrated with heat pipes and extended surface using numerical modelling, wind tunnel and far-field testing in the UAE during a summer month. Results of the scaled wind tunnel tests showed that the addition of the heat pipes and extended surfaces reduced the airflow through the windcatcher but did not impede the flow even at low outdoor wind speeds, this was further confirmed by the smoke visualisation tests. Analysis of pollutant concentration in the building model showed that the proposed windcatcher configuration was capable of delivering fresh air at a sufficient rate to lower CO2 concentration levels below the recommended guidelines for air quality. The thermal comfort analysis was conducted and it was observed that for the present design, equal distribution of thermal comfort was not achieved due to combination of high air movement, colder temperature and high humidity below the windcatcher which resulted in higher thermal discomfort in this area. Further work is required to develop a suitable control strategy in the form of volume control dampers which would enable the supply flowrate to be monitored and altered as required and optimized the distribution in the occupied space. Field tests data was used to validate the numerical modelling, showing good agreement between both methods.

The present study investigated the potential of the application of a live occupancy detection approach to assist the operations of demand-controlled ventilation (DCV) systems to ensure that sufficient interior thermal conditions and air quality were attained while reducing unnecessary building energy loads to improve building energy performance. Faster region-based convolutional neural network (RCNN) models were trained to detect the number of people and occupancy activities respectively, and deployed to an artificial intelligence (AI)-powered camera. Experimental tests were carried out within a case study room to assess the performance of this approach. Due to the less complexity of people counting model, it achieved an average intersection over union (IoU) detection accuracy of about 98.9%, which was higher than activity detection model of about 88.5%. During the detection, the count-based occupancy profiles were produced according to the real-time information about the number of people and their activities. To estimate the effect of this approach on indoor air quality and energy demand, scenario-based modelling of the case study building under four ventilation scenarios was carried out via building energy simulation (BES). Results showed that the proposed approach could provide demand-driven ventilation controls data on the dynamic changes of occupancy to improve the indoor air quality (IAQ) and address the problem of under- or over-estimation of the ventilation demand when using the static or fixed profiles.

Pure organic luminogens with long-persistent luminescence have been extensively studied, on account of their fundamental research significance and diverse utilizations in anticounterfeiting, bioimaging, encryption, organic light-emitting diodes, chemo-sensing, etc. However, time-dependent color-tunable afterglow is rarely reported, especially for single-component materials. In this work, we reported an organic luminogen with time-dependent afterglow, namely, benzoyleneurea (BEU), with multiple persistent room-temperature phosphorescence (p-RTP) and thermally activated delayed fluorescence (TADF) in single crystals. While the lifetime of TADF is relatively short (~1.2 ms), those for p-RTP are as long as around 369~754 ms. The comparable but different decay rates of diversified p-RTP emissions endow BEU crystals with obvious time-dependent afterglow. The existence of multiple emissions can be reasonably illustrated by the clustering-triggered emission (CTE) mechanism. Single-crystal structure illustrates that the combination of benzene ring and nonconventional chromophores of ureide helps facilitate divergent intermolecular interactions, which contribute to the formation of varying emissive species. Moreover, its methyl- and chloro-substituted derivatives show similar multiple p-RTP emissions. However, no time-dependent afterglows are observed in their crystals, due to the highly approaching lifetimes. The afterglow color variation of BEU crystals grants its applications in advanced anticounterfeiting field and information encryption.

Occupancy behaviour in buildings can impact the energy performance and operation of heating, ventilation and air-conditioning (HVAC) systems. HVAC, which uses conventional control strategies or “fixed” setpoint schedules, could not adjust to the conditioned spaces' actual requirements, resulting in building spaces being over or under-conditioned. While the unintended opening of windows can lead to substantial heat loss and consequently raises energy consumption. To optimise building operations, it is necessary to employ solutions such as demand-driven controls, which can monitor the utilisation of indoor spaces and provide the actual thermal comfort requirements of occupants. This study presents a novel vision-based deep learning framework for occupancy activity detection and recognition including the manual window operations in buildings. A region-based Convolutional Neural Network (R-CNN) model was trained and deployed to a camera for real-time detection and recognition. Based on the field experiments conducted within a case study University building, overall accuracy of 85.63% was achieved for occupancy activity detection and 92.20% for window operation detection. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable HVAC systems to respond to dynamic changes within indoor spaces. Results showed that the proposed approach could reduce the over-or under-estimation of occupancy heat gains compared with the use of “fixed” or static profiles. In addition, the approach can help alert building users or managers about windows left open unintentionally, which can reduce unnecessary ventilation heat losses. Furthermore, the approach can also predict the room CO2 concentration and advise occupants about a suitable natural ventilation strategy. The study highlighted the potential of the multi-purpose detection approach, but further development is necessary, including optimisation of the deep learning model, full integration with HVAC controls and further model training and field testing.

The influence of the foliage boron(B)-spaying concentration on the yield and fatty acid composition of tree peony (Paeonia ostii ‘Feng Dan’) was explored in the experiment of 2014-2015. In this research, a statistically significant correlation was found between the mass concentration of foliage boron- spraying and tree peony seed yield. Maximum yield increment of tree peony seeds was obtained when the boron-spraying concentration was 4 g/L. The composition of fatty acid was analyzed by gas chromatography-mass spectrometry (GC-MS), and the main components of the fatty acid composition were quantified by internal standard method. The increase in the oil extraction rate after foliage boron-spraying nutrition of different concentration was significant and ranged between 0.04% and 11.43%. Seed oil production and oil extraction rate were also increased due to the increase of seed yields. Furthermore, foliage boron-spraying had a significant effect on the content of linolenic acid and linoleic acid in seed oil.

This paper introduces a vision-based deep learning approach that enables the detection and recognition of occupants’ activities within building spaces. The data can feed into building energy management systems through the establishment of occupancy heat emission profiles, which can help minimise unnecessary heating, ventilation, and air-conditioning (HVAC) energy loads and effectively manage indoor conditions. The proposed demand-driven method can enable HVAC systems to adapt and make a timely response to dynamic changes of occupancy, instead of using “static” or fixed occupancy operation schedules, historical load, and time factor. Based on a convolutional neural network, the model was developed to enable occupancy activity detection using a camera. Training data was obtained from online image sources and captured images of various occupant activities in office spaces. Tests were performed by real-time live detection and predictions of occupancy activities in buildings. Initial activities response includes sitting, standing, walking, and napping. Average detection accuracy of 80.62% was achieved. The detection formed the real-time occupancy heat emission profiles known as the Deep Learning Influenced Profile. Along with typical ‘scheduled’ office occupancy profiles, a building energy simulation (BES) tool was used to further assess the framework. An office space in Nottingham, UK was selected to test the proposed method and modelled using building simulation. Using the deep learning detection method, the results showed that the occupancy heat gains could be represented more accurately in comparison to using static office occupancy profiles. The accurate detection of occupants and their activities can also be used to effectively estimate CO2 concentration. The information can be useful for modulating ventilation systems leading to better indoor environmental quality. Overall, this initial approach of the study showed the capabilities of this framework for detecting occupancy activities and providing reliable predictions of building internal gains.

Building energy consumption accounts for a large proportion of energy use globally. Previous works have shown that a large amount of energy is wasted in under- or over-utilized spaces since typical building management systems function based on fixed or static operation schedules. While the presence of occupants and how they use equipment contribute to the internal energy demand and affect the thermal environment. Office buildings are likely to have higher cooling demands in the future due to increasing use of equipment, emphasising the need to develop systems which can better understand (and reduce) the impact of internal gains from equipment and adapt to actual requirements. This project aims to develop a deep learning-based approach which enables the detection and recognition of equipment usage and the associated heat emissions in office spaces. Subsequently, the data can be fed into building energy management systems through the formation of equipment heat gain profile; therefore, building energy usage can be effectively managed. Experiments were conducted in typical offices to generate the corresponding heat gain profiles, and then these were used in building simulation software to assess building performance. It was found that the model can perform equipment detection with an accuracy of 89.3%. While maintaining thermal comfort levels, up to 19% annual cooling energy demand reduction can be achieved by the proposed strategy when compared to that for the building managed by a static scheduled heating, ventilation and air-conditioning system, where in the studies, we focus on three types of equipment - computer, printer and kettle that are widely used in the office buildings. The findings indicate that it is feasible to use the deep learning approach to predict equipment heat emission for achieving effective building energy management therefore to reduce building energy demand.

The present work will develop a learning-based approach for a demand-driven control system which can automatically adjust the Heating, Ventilation and Air Conditioning (HVAC) setpoints and supply conditions in terms of the actual requirements of the conditioned space. Internal heat gains from typical office equipment, such as computers, printers and kettle will be the focus of this paper. Due to its irregular use during scheduled heating or cooling service periods, an opportunity is offered to reduce unnecessary energy demands of HVAC systems related to the actual use of the equipment and its heat gains, i.e. over- and under-utilization of equipment indicate whether interior spaces are required to be conditioned or not. The work will be using deep learning enabled cameras which can locally run trained algorithms to analyse and take action based on how equipment is utilised in a space. This proposed strategy automatically responds to the equipment usage for optimising energy consumption and indoor conditions. The work will compare the performance of the developed approach with a conventional approach such as the use of static heating or cooling profiles. To highlight its capabilities, the approach is applied to detect the equipment usage in a real open plan office and the output (i.e. deep learning profile) is used as input for a building energy simulation model. The initial results showed that while maintaining thermal comfort levels, up to 19% reduction of the annual energy consumption can be achieved by employing the proposed strategy in comparison to conventionally-scheduled HVAC systems, while only focusing on three types of equipment.

To enable heating, ventilation and air-conditioning systems to effectively work for the next generation-built environment by reducing unnecessary energy loads while also maintaining satisfactory thermal comfort conditions, this present work introduces a demand-driven deep learning-based framework, which can be integrated with building energy management systems and provide accurate predictions of occupancy activities. The developed framework utilises a deep learning algorithm and an artificial intelligence-powered camera. Tests are performed with new data fed into the framework which enables predictions of typical activities in buildings; walking, standing sitting and napping. Building energy simulation was used with various occupancy profile schedules: two typical static office occupancy profiles, a schedule generated via the deep learning framework and an actual prediction profile. An office space within a case study building was modelled. Initial results showed that the overall occupancy heat gains were up to 30.56% lower when the deep learning generated profile was used; as compared to the static office occupancy profile. This indicated a 0.015 kW decrease in occupancy gains, which also influenced the increase in building heating loads. Analysis indicates the occupancy detection-based framework is a potential solution for the development of effective heating, ventilation and air-conditioning systems. Additionally, the requirement for the deep learning framework to work for multiple occupancy activity detection and recognition was identified.

This study used high-performance liquid chromatography to measure 202 teicoplanin plasma trough concentrations (Cmin ) in 114 haematological malignancy patients with febrile neutropenia. Patients were divided into two groups according to the mean initial dose (MID) over the first 3 days of treatment: (i) MID = 533.33 mg/day (loading dose group, 400 mg q12h for three doses followed by 400 mg qd, n = 62) and (ii) MID 10 mg/L (54.5% versus 11.1%, p = 0.001), and the probability of Cmin 10 mg/L rapidly and, from a pharmacokinetic/pharmacodynamic perspective, 600 mg is recommended as loading and maintenance dose for these patients when AUC24 /minimum inhibitory concentration > 345.

Opioid mechanisms are involved in the control of water and NaCl intake and opioid receptors (ORs) are present in the central nucleus of the amygdala (CeA), a site of important facilitatory mechanisms related to the control of sodium appetite. Therefore, in the present study we investigated the effects of the activation of μ-ORs in the CeA on 0.3 M NaCl and water intake in rats. Male Sprague–Dawley rats with stainless steel cannulas implanted bilaterally in the CeA were used. In rats submitted to water deprivation–partial rehydration, bilateral injections of the selective μ-OR agonist [D-Ala 2 , N-Me-Phe 4 , Gly 5 -ol]-enkephalin (DAMGO) in the doses of 1, 2, and 4 nmol into the CeA induced a dose-related increase of 0.3 M NaCl intake and water intake, and bilateral injections of the selective μ-OR antagonist D-Phe-Cys-Trp-Arg-Thr-Pen-Thr-NH 2 (CTAP) in the doses of 0.5, 1, and 2 nmol into the CeA produced a dose-related decrease of 0.3 M NaCl and water intake induced by DAMGO 2 nmol into the same site. In rats treated with the diuretic furosemide (10 mg/kg b.w.) combined with the angiotensin-converting enzyme inhibitor captopril (5 mg/kg b.w.) injected subcutaneously, bilateral injections of DAMGO 2 nmol into the CeA increased 0.3 M NaCl intake and water intake and the blockade of μ-ORs with CTAP 1 nmol injected into the CeA reduced the increase in 0.3 M NaCl intake and water intake induced by DAMGO 2 nmol into the same site. Bilateral injections of DAMGO into the CeA did not change urinary volume, sodium urinary excretion and mean arterial pressure, but increased activity. Thus stimulating μ-ORs in the CeA increases hypertonic sodium intake, whereas antagonizing these sites inhibits hypertonic sodium intake. Together, our results implicate μ-ORs in the CeA in a positive regulation of sodium intake.

Component development technology can guarantee system' independence, minimize the system's coupling between software and hardware, enhance the software reusability, and then reduce the software development cost. This paper presents a method to develop a communication interface component which encapsulates an Inspection Plus-plus (I++) command management function with the above features. The component is designed for handling bi-directional communication between the measuring software and the measuring equipment via a set of encapsulated API (Application Programming Interface). As a result, it can effectively separate the software from measuring machine. The test result shows that the component can work well with the other modules of DirectDMIS, a set of CMM (Coordinate Measuring Machine) software developed by Precision Measuring Technology Lab of Shanghai Second Polytechnic University, where a real CMM equipped with a controller system developed based on I++ was involved.

Reusable Component Technology (RCT) allows program of encapsulating similar behaviors that affect multiple modules into a sole module which can be reused by the related applications. CMM (Coordinate Measuring Machine) software is a relevant complicated system which normally consists of many modules and be suited to apply RCT. This paper presents a method with RCT to support data communicating among different modules which written with different programming languages, e.g., C, C++ and C#, to support CMM software upgrade effectively and efficiently. A C# software platform design is discussed with RCW (Running Callable Wrapper) agent by which can make data encapsulating and sending/receiving available between C# code and non C# code; and then the function model and components definition are presented where the functions and interfaces of individual component are analyzed respectively; finally, a real case of CAD model management component is illustrated in a real CMM software development.

Computer aided instruction (CAI) technology has been proved that it can strongly support teaching in improving class quality, students' studying efficiency and effectiveness. In terms of the requirement of general vehicle design course, this paper proposes a way to this kinds of course teaching by offering a practical teaching tool, which is based on supporting multi-engineering modeling and simulation. It is designed to supporting Modelica modeling, which in an object-oriented modeling language and is suitable for a dynamic system modeling and simulation. In the teaching tool implementation, it is realized by integrating MWorks modeling system, a modeling system based on Modelica developed by CAD Center of Huazhong University of Science and Technology (CCHUST), China. The methods of teaching material preparation, teaching tool architecture design and implementation are discussed as well. The practice proves that the proposed method is feasible and can be extended to the other mechanical engineering courses teaching with the similar simulation requirement.

This paper presents a novel way to develop a software tool for the mechanical 3D animation CAI courseware design in terms of the characteristics and the dedicated requirement of the mechanical major courses teaching. It applies the HOOPS components developed by USA HOOPS Ltd. and using the component as a kernel graphic display engine. The three main components of the software tool are discussed in the paper on how to develop with the COM (Component Object Model) technology, i.e., the independent 3D graphic display component, a 3D courseware design process manager which is used to record and replay the design 3D animation scheme, and the last one, a math library which encapsulates the common math calculation equations about matrix elements and vector elements. A prototype system for the mechanical 3D animation CAI courseware design has been developed that is capable of the interoperable feature and its feasible and effective has been verified by some practical cases.

The implementation of MBD/MBE (Model-based Design and Engineering) in the product design and manufacturing can effectively support multi-step data interoperation among “Design-Manufacture-Measurement.” Due to the limited data interoperation functions provided by the current CMM software (Coordinate Measurement Machine), most studies of MBD/MBE focused on designing the upstream. In contrast, the downstream (the underlying measurement data utilization) research is less. MES has an essential function in managing the manufacturing quality that analyses the condition and its development trend by collecting the manufacturing process quality data. Insufficient use of the underlying measurement data will lead to limited MES functions, especially for the capability of decision-making in intelligent manufacturing systems. The paper presents a measurement data interoperation method based on the interoperation layer method to support the quality analysis in MES (Manufacturing Execution System), discusses the relevant critical logic and data processing flow of the layer. It is verified that it can provide more comprehensive measurement data for quality management in the workshop.

The data interoperation between VF (virtual factory) platform and MES (Manufacturing Execution System) plays an important role in intelligent factory construction. The study focuses on the integration strategy between the VF and the MES by incorporating VF manufacturing assets in two ways, i.e., vertical integration (used for production line performance evaluation) and the horizontal integration (cloud manufacturing based on manufacturing assets services discovery and their composition). The VF platform which integrates the manufacturing assets in two manners is designed as the bottom layer in the entire integration framework. It has been applied to build a four tiers integration model in an intelligent production system construction of a domestic ship manufacturer and verified its feasibility and availability.