Process Modules Research Articles

Application of long short-term memory recurrent neural networks for localisation of leak source using 3D computational fluid dynamics

Gas leaks represent a major concern in industrial sites due to potential human and economical losses. Prompt identification of leak scenarios favours corrective maintenance avoiding the domino effect. In this paper, long short-term memory recurrent neural networks were trained and tested to CH4 leakage source in a chemical process module. We exploit the benefits of varying the temporal length of input variables, and the datasets were obtained employing 3D-CFD simulations. We consider four leak locations, four wind speeds, and eight wind directions, besides the non-leakage scenario for the same wind speeds and directions. The models were trained using different values of timesteps to evaluate the prediction accuracy for unseen data. Results showed progressive improvement of the performance of the models with greater values of timesteps, and good generalisation with test accuracy over 95.3%, indicating the ability of the model to correctly predict the leakage source using easily monitored variables.

Systematic derivation of customized development sprints for an agile development of wind turbines

To account for the increasing volatility and uncertainty of external requirements, mechanical and plant engineering companies are facing growing demands for increased flexibility and adaptability within product development. The wind turbine industry in particular, which is characterized by low unit numbers, long development time and high development costs, is especially affected by this trend. Companies are forced to implement processes that allow for short-term adaptation to changing requirements. Plan-driven approaches typically presume that requirements are defined in the early stages of product development and remain static throughout the process. Companies are increasingly resorting to agile approaches, which rely on an incremental and highly iterative development process divided into short sprints. However, the main challenge for them is moving from plan-driven to agile due to the lack of a comprehensive method of incorporating and transforming their current development activities into an agile approach. This allows for the configuration of agile sprints on a short-term, results-oriented basis from process modules identified in the current development. A five step method for this purpose is being presented. The five steps are as follows: (1) Process analysis to record the status as-is; (2) Identification of recurring process modules throughout the development process; (3) Identification of development objects that are used or generated by the process modules; (4) Bottom-up classification and consolidation of these development objects; (5) Configuration of development sprints as a sequence of process modules linked via development objects. The respective steps were validated in cooperation with a company from the wind turbine industry.

Preparation of Papers for IFAC Conferences & Symposia: Activity combination and optimization method to support rapid establishment of product Research & Development process

Aiming at the problems with low efficiency and low standardization in the establishment of complex product Research & Development(R&D) process, a combination and optimization method for R&D activities is proposed in this paper. Firstly, process module is established by combining related R&D activities using process network diagram and description text. Then, a method is established for similarity representation between process module and design requirements, on which the module reorganization is based. And optimal combination scheme of modules is obtained by an adaptive genetic algorithm. Finally, the effectiveness of proposed method is verified through the example of a product R&D process.

Numerical Simulation of Decompression Process of a Mars Rover in the Launch Phase

This paper performs numerical simulation on the decompression process of a Mars rover using FLUENT. The pressure differential between the inside and outside of the Mars rover resulting from changes in ambient pressure of the rocket fairing is investigated. In terms of numerical simulation, PROFILE outlet boundary conditions are developed and the impacts of ambient pressure settings, time steps, and mesh density are investigated to improve the accuracy of simulation results. The decompression process of the separate large module, large and small modules under two types of ambient pressures are simulated. The results show that the largest pressure differential between the inside and outside of the module body is less than 2200 Pa. Because of the small size of the small module, the results for the separate large module and the large/small modules are consistent. The pressure differential between the inside and outside of the rover is mainly influenced by the variation in ambient pressure.

Machine learning for predictive maintenance of photovoltaic panels: cleaning process application

It is well known that the presence of dust on the surface of PV modules has a significant impact on their efficiency. Then, the consequent reduction in energy production has a non-negligible effect on the incomes. Although there is a growing need for accurate data showing where the solar arrays need maintenance in this rush for renewable energy. The purpose of this article is to introduce the research on existing photovoltaic panel maintenance solutions and introduce a new machine learning algorithm application to minimize the cleaning process of photovoltaic modules.

A Case Study of a Small Switching Power Supply Process: Based on the Module of Production

With the rapid development of electronic products, technology plays an important role in the management and production of electronic products. In the increasingly fierce market competition, excellent technological process is also one of the important bargaining chips for the success of an enterprise, and it is the embodiment of the level of enterprise management ability. As a product of modern power electronic technology, switching power supply represents the development direction of regulated power supply, and it is an industry pursued by many electronic companies. Switching power supply manufacturers should not only pay attention to the miniaturized power module technology, but also do a good job in the quality management of the production process. Only by doing a good job in the production process design, can better ensure the function and quality of the miniaturized switching power supply module, and help enterprises gain an advantage in the market competition. Using the method of case study, this article interviewed the small switching power supply design engineers and production workshop workers in Factory A. combined with literature analysis and on-site observation, it is proposed that the production process of switching power supply module should involve three stages: before production, during production, and after production, which include 8 links: production preparation, coil production, the device welding assembly, cleaning, inspection, potting, sealing and marking. In particular, the requirements and precautions of each step are described in detail.

Applying Taguchi Method to Optimize Vacuum Printing Encapsulation Process

Under the prosperity of the global electronics industry, the current market requires consumer products, such as smartphones, notebook computers, tablet computers, and other portable electronic products, to become lighter, thinner, shorter, and smaller. The miniaturized design of the System in Package technology (SiP) integrates multiple different chips, such as an array package (BGA/CSP) or a peripheral package (QFP/TSOP), as well as passive components, such as resistors and capacitors, into a single package module. The Vacuum Printing Encapsulation System (VPES) features cost-efficiency and flexibility advantages, as compared with traditional transfer molding and compression molding, and thus, improves product reliability. However, as VPES is an emerging process, the impact of relevant VPES parameters has not yet been determined. Moreover, it has been discovered that there are still air bubbles in the cured Epoxy Molding Compound (EMC). In this study, the Taguchi experimental design is applied to propose the optimal process parameter combination, including the squeegee moving speed: 20mm/s, the printing delay time: 20s, the number of squeegee printings: three, and the inner chamber vacuum: 3KPa. The results of variance analysis show that the contributions of the number of squeegee printings and the inner chamber vacuum to the air bubble rate of the EMC adhesive material are 76.38% and 17.75%, respectively, which indicates that they have significant impact. The results of confirmation testing show that the above optimal VPES process parameters can minimize the air bubble rate occurring in the EMC adhesive material and avoid the risks of short circuits in the tin bridge during the reflow process of the SiP module.

Three-dimensional Measurement Method of Glue Surface During the Mounting Process of Micro-camera Module

Three-dimensional Measurement Method of Glue Surface During the Mounting Process of Micro-camera Module

Retracted] Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology

Aiming at the problems of slow teaching resource sharing rate, long platform response time, and low student learning efficiency in traditional ideological and political learning platforms, a research on the construction of intelligent media ideological and political learning platforms based on artificial intelligence technology is proposed. We build an artificial intelligence open source development platform framework based on the cloud platform and use the Ceph method to optimize the storage of artificial intelligence training platform data. Under this framework, we design the business process and business module service architecture of the intelligent media ideological and political learning platform. Based on the K‐means algorithm, the intelligent media ideological and political learning platform resource management module is designed, the teaching resource database is constructed, and the teaching resource sharing model component module is designed to realize the construction of the intelligent media ideological and political learning platform. The experimental results show that the sharing rate of ideological, political, and educational learning resources on the platform is relatively fast. The response time of the platform is 0.08 s when the amount of ideological and political teaching resources is 16000 MB. Students who are interested and very interested in the teaching account for 89% of the total.

System Dynamic Theoretical Framework for Construction Management: A Case of Baltic States

Abstract As a result of the rapid development of the construction industry, in recent years, research has increasingly used system dynamics (SD) modelling to determine the positive and negative causal feedback in various management processes. Given SD diverse approach to the management of construction companies and projects, it would be necessary to develop a framework for a system dynamics model (SDM) incorporating the main processes of the Baltic States by identifying and providing a systematic understanding of their distribution. Using literature analysis, this study provides the results of 79 selected scientific literature sources from 1991 to 2020. The obtained information is structured according to the annual volume of publications, country of affiliation, the most successful authors and the most popular scientific journals on the topic of the SD. With the assistance of bibliometric analysis and co-occurrence of keywords, system dynamics management processes in construction companies were structured, choosing the following separate elements: (1) project planning, (2) project management, (3) risk management, (4) project performance, (5) project productivity, (6) sustainability. Using a systematic approach, according to the framework of the model, the characteristics of each management process were classified and identified, dividing them into subgroups. The results of the analysis show that the overview of some SD components or processes is mostly provided, which emphasises its aspects and usability in company management, thus indicating the need to identify the framework of the process module. The introduction of the SD process framework in the company would gradually create a competitive advantage in market conditions.

Weighted Gene Coexpression Network Analysis in Mouse Livers following Ischemia-Reperfusion and Extensive Hepatectomy.

In mouse models, the recovery of liver volume is mainly mediated by the proliferation of hepatocytes after partial hepatectomy that is commonly accompanied with ischemia-reperfusion. The identification of differently expressed genes in liver following partial hepatectomy benefits the better understanding of the molecular mechanisms during liver regeneration (LR) with appliable clinical significance. Briefly, studying different gene expression patterns in liver tissues collected from the mice group that survived through extensive hepatectomy will be of huge critical importance in LR than those collected from the mice group that survived through appropriate hepatectomy. In this study, we performed the weighted gene coexpression network analysis (WGCNA) to address the central candidate genes and to construct the free-scale gene coexpression networks using the identified dynamic different expressive genes in liver specimens from the mice with 85% hepatectomy (20% for seven-day survial rate) and 50% hepatectomy (100% for seven-day survial rate under ischemia-reperfusion condition compared with the sham group control mice). The WGCNA combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses pinpointed out the apparent distinguished importance of three gene expression modules: the blue module for apoptotic process, the turquoise module for lipid metabolism, and the green module for fatty acid metabolic process in LR following extensive hepatectomy. WGCNA analysis and protein-protein interaction (PPI) network construction highlighted FAM175B, OGT, and PDE3B were the potential three hub genes in the previously mentioned three modules. This work may help to provide new clues to the future fundamental study and treatment strategy for LR following liver injury and hepatectomy.

SkinKit

The emergence of on-skin interfaces has created an opportunity for seamless, always-available on-body interactions. However, developing a new fabrication process for on-skin interfaces can be time-consuming, challenging to incorporate new features, and not available for quick form-factor preview through prototyping. We introduce SkinKit, the first construction toolkit for on-skin interfaces, which enables fast, low-fidelity prototyping with a slim form factor directly applicable to the skin. SkinKit comprises modules consisting of skin-conformable base substrates and reusable Flexible Printed Circuits Board (FPCB) blocks. They are easy to attach and remove under tangible plug-and-play construction but still offer robust conductive connections in a slim form. Further, SkinKit aims to lower the barrier to entry in building on-skin interfaces without demanding technical expertise. It leverages a variety of preprogrammed modules connected in unique sequences to achieve various function customizations. We describe our iterative design and development process of SkinKit, comparing materials, connection mechanisms, and modules reflecting on its capability. We report results from single- and multi- session workshops with 34 maker participants spanning STEM and design backgrounds. Our findings reveal how diverse maker populations engage in on-skin interface design, what types of applications they choose to build, and what challenges they faced.

Research on Energy-Saving Optimization of Rural Houses in Specific Regions Based on Internet of Things

With the expansion of social energy use, the optimization of residential energy conservation has become urgent and important. The research on rural housing in areas with hot summers and cold winters started late and is relatively backward in terms of housing energy saving, which greatly hinders the sustainable development of rural areas. The application of the Internet of Things technology helps to provide a stable technical guarantee for energy-saving optimization. Therefore, this paper takes the energy-saving optimization design of rural houses in hot summer and cold winter areas based on the Internet of Things technology as the research theme. This article first takes H rural area as the research object and analyzes the climatic conditions of the place, that is, the typical characteristics of hot summer and cold winter. Then, the intelligent temperature acquisition system is designed, and the working process and main hardware modules of the system are introduced. This text combines GPRS and Internet of Things technology, designs the temperature control system, and carries on the test to the system operation effect. The test results show that during the heating period in January, before and after the temperature control system controls the room temperature, the maximum relative error between the set temperature value and the actual temperature value is 0.49 and 0.27, respectively. It can be seen that the intelligent temperature energy-saving control system can collect the indoor temperature in real time and can well control the heating equipment.

Multi-scale deep adversarial network for particle detection in liquid crystal module

Conductive particle inspection is the crucial procedure in the circuit detection process of liquid crystal modules since only validly deformed particles have conductive effects in the circuit. The main task of inspection is to accurately locate and count the validly deformed particles. However, due to various difficulties such as the variety of deformed particles, the aggregation between valid and invalid particles, the different sizes to overlap of deformed particles, and uneven illumination, etc., robust real-time detection of valid particles becomes a challenging problem. In this paper, a novel Multi-Scale Deep Adversarial Network (MSDA-Net) composed of Generator and Discriminator is proposed for detecting valid particles in a liquid crystal module. Firstly, a compact Multi-branch network is adopted as Generator for detecting valid particles, which achieves well detection accuracy with less time by utilizing the lightweight structure and the Coarse-to-Fine multi-scale feature extraction form. Secondly, the designed Logical Focusing Attention module (LFA) is applied in Generator to enhance the center location precision of valid particles. Furthermore, the Multi-branch based Deep Adversarial Strategy is proposed to distinguish deeply the similarity of visual characteristics between valid and invalid particles, which achieves higher detection accuracy with fewer fake particles. The experiments on the real datasets demonstrate the effectiveness of the proposed methods for the real-time detection of valid particles compared to the state-of-the-art methods.

LIGHTWEIGHT AND MULTI-PURPOSE PARTITION FOR VERTICAL HOUSING

Vertical housings have been developed to solve housing quality degradation due to rapid urbanization. These housings are often lack of privacy and comfort, since the lay-out mostly consists of one open plan space with limited area. This paper presents a lightweight and multi-purpose partition to improve the living quality in vertical housings. The lightweight characteristic is intended to minimize structural loads, while the multi-purpose property allows the partition to be transformed into some furniture for spatial efficiency. A literature review and material surveys were performed prior to material selection and the design process. Then, physical model experiments were carried out to simulate the installation process and the transformation process of the partition modules into some furniture. Result show that the partition’s weight is only 11-36.7% of the common partition wall, and when installed, the transformable property enables an adaptable space which can be adjusted to the occupants’ different conditions.

Precision and intelligent agricultural decision support system based on big data analysis

ABSTRACT In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects.

Experimental study on thermal runaway characteristic parameters of parallel battery modules

This work introduces a method for measuring the main physical quantities, such as short-circuit current and capacity loss, in the thermal runaway process of the parallel battery module and presents detailed analysis and discussion on a 25 Ah commercial prismatic lithium-ion ferrous phosphate (LFP) battery. By establishing a basic circuit model of the thermal runaway process of the parallel battery module, experiments were conducted to observe the thermal runaway process of the parallel battery module. The parameters related to the electrothermal effect in the thermal runaway process were obtained by either direct measurement or calculation. The results showed that the short-circuit resistance of the cell, which was triggered to thermal runaway changed dramatically. The minimum resistance was about 0.5 mΩ, and the peak current of discharge was more than 1100 A. During the stable discharge process of the other parallel cells to the thermal runaway cell, the short-circuit resistance was about 10 mΩ. In the stable discharge stage, the electric heating power was about 600 to 900 W. However, the power of thermal runaway reaction from the cell itself was still more than 30 times the heating power of the electrothermal effect, exceeding 21 kW. The accuracy of the measurement method and analysis method was also verified by comparing the same parametric data obtained using different methods. The results showed that the method is feasible for studies on the mechanism and simulation of thermal runaway of parallel battery modules.

Carbon and water footprints assessment of cotton jeans using the method based on modularity: A full life cycle perspective

Cotton jeans with huge market demand have a great potential negative impact on the ecological environment, especially in terms of climate change and water depletion. Given the complexity and difficulty of carbon and water footprints assessment in the textile sector due to the plethora of material varieties and life cycle processes involved, previous researchers have embraced process modularity (i.e., decompose life cycle processes into components) as an auxiliary technique. Although modularity enhances overall flexibility and assessment efficiency, a holistic view is missing for the comprehensive consideration of both product production and consumption processes. Moreover, extant studies only take a limited or even a single manufacturing process as a case demonstration, rather than examining the applicability of the modular method throughout the product life cycle. It is therefore unclear how the interaction and assembly of different process modules will unfold when multiple stages are involved. Accordingly, this study extended the research boundary to the whole life cycle of textile products, and verified the feasibility and practicality of the method with a computational case study of a pair of cotton jeans. The results showed that the total impacts of carbon footprint, water scarcity footprint, water eutrophication footprint and water ecotoxicity footprint were 90.37 kg CO2 eq, 13.74 m3 H2O eq, 1.67 × 10−2 kg PO43− eq and 112.41 m3 H2O eq respectively; and finishing, cotton cultivation and laundering processes were major contributors to these environmental impacts. The study also demonstrated how different cotton jeans parameter values from the life cycle affect the carbon and water footprints through sensitivity analysis. Furthermore, based on the decomposed modules, 12 common use patterns in China were discussed, which confirmed the superiority of the modular method without recalculation from scratch. The scenario analysis revealed that a combination of top loader machine washing and line drying once a month without ironing for cotton jeans within a two-year lifespan was the most promising alternative. The results obtained in this study can provide methodological and technical guidance for follow-up research and application. In the future, other product categories and impact indicators can be integrated into the modular method.

Design and Development of Matter Module with the Integration of Computational Thinking by Employing ADDIE Model

This study aims to explore the design and development process of the Matter module for secondary school students. The needs analysis shows a similar perception between students and teachers who described the matter topic as complex due to the abstract nature of the Matter topic. This module emphasises the use of animation in visualising particles in different matter states. The module was designed to make learning more active and meaningful by integrating computational thinking skills through cooperative and project-based learning. Through this approach, students are actively involved in building animation projects to visualise the movement of particles by applying computational thinking concepts (abstraction, decomposition, generalisation, algorithm, and evaluation) as they engage with programming in Scratch. Thus, to systematically design and develop the Matter module, the ADDIE model was employed. The design and development of the module comprise five phases, Analysis, Design, Development, Implementation, and Evaluation. The module was evaluated by experts using a formative evaluation approach. The results show that the validity of the Matter module is high and can support the integration of computational thinking skills.

Development of the Modularity Measure for Assembly Process Structures

This study is aimed at exploring the problem of quantification of process modularity degree. Modularity as a system design principle is apprehended here as the extent to which processes can be decomposed into modules to be executed in parallel and/or in series. For this purpose, a new method is proposed to measure relative modularity of different assembly process structures. This method is compared with other relative modularity measures, namely singular value modularity index, degree of process module, and process module independence, in order to verify its effectiveness. For this purpose, selected representative types of assembly process structures are used. This testing proved that the proposed relative modularity indicator for manufacturing and/or assembly process structures reflects the expected system property in adequate way.