Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, ma...Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.展开更多
Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus...Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.展开更多
Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers,manufacturers,suppliers,and consumers....Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers,manufacturers,suppliers,and consumers.Each stakeholder has implemented different technology-based systems to record and track product transactions.However,these systems work in silos,and there is no intra-system communication,leading to a lack of complete supply chain traceability for all apparel stakeholders.Moreover,apparel stakeholders are reluctant to share their business information with business competitors;thus,they involve third-party auditors to ensure the quality of the final product.Furthermore,the apparel manufacturing industry faces challenges with counterfeit products,making it difficult for consumers to determine the authenticity of the products.Therefore,in this paper,a trustworthy apparel product traceability framework called ChainApparel is developed using the Internet of Things(IoT)and blockchain to address these challenges of authenticity and traceability of apparel products.Specifically,multiple smart contracts are designed and developed for registration,process execution,audit,fault,and product traceability to authorize,validate,and trace every business transaction among the apparel stakeholders.Further,the real-time performance analysis of ChainApparel is carried out regarding transaction throughput and latency by deploying the compute nodes at different geographical locations using Hyperledger Fabric.The results conclude that ChainApparel accomplished significant performance under diverse workloads while ensuring complete traceability along the complex supply chain of the apparel industry.Thus,the ChainApparel framework helps make the apparel product more trustworthy and transparent in the market while safeguarding trust among the industry stakeholders.展开更多
Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology th...Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.展开更多
近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数...近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。展开更多
Industrial Control Systems(ICS)and SCADA(Supervisory Control and Data Acquisition)systems play a critical role in the management and regulation of critical infrastructure.SCADA systems brings us closer to the real-tim...Industrial Control Systems(ICS)and SCADA(Supervisory Control and Data Acquisition)systems play a critical role in the management and regulation of critical infrastructure.SCADA systems brings us closer to the real-time application world.All process and equipment control capability is typically provided by a Distributed Control System(DCS)in industries such as power stations,agricultural systems,chemical and water treatment plants.Instead of control through DCS,this paper proposes a SCADA and PLC(Programmable Logic Controller)system to control the ratio control division and the assembly line division inside the chemical plant.A specific design and implementation method for development of SCADA/PLC based real time ratio control and automated assembly line system in a chemical plant is introduced.The assembly line division is further divided into sorting stage,filling stage and the auxiliary stage,which includes the capping unit,labelling unit and then the storage.In the ratio control division,we have defined the levels inside the mixer and ratio of the raw materials through human machine interface(HMI)panel.The ratio of raw materials is kept constant on the basis of flow rates of wild stream and manipulated stream.There is a flexibility in defining new levels and the ratios of the raw materials inside the mixer.But here we taken the predefined levels(low,medium,high)and ratios(3:4,2:1,2:5).Control valves are used for regulating the flow of the compositions.In the assembly line division,the containers are sorted on the basis of size and type of material used i.e.,big sized metallic containers and small sized non-metallic containers by inductive and capacitive proximity sensors.All the processes are facilitated with laser beam type or reflective type sensors on the conveyor system.Building a highly stable and dependable PLC/SCADA system instead of Distributed Control System is required to achieve automatic management and control of chemical industry processes to reduce waste manpower and physical resources,as well as to improve worker safety.展开更多
The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industri...The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industrial transfer of energy-intensive manufacturing in the eastern,central,and western regions since the policy of large-scale development of western China was implemented.We measured the total factor productivity(TFP)of western China using the DEAMalmquist index method.We conducted a regression analysis to measure the effect of western China’s undertaking of the transfer of the energy-intensive manufacturing industry.The findings of this study show that during 2000–2019,eleven provinces(as well as autonomous regions and municipalities)in western China undertook the transfer of the energy-intensive manufacturing industry from the eastern and central regions to varying degrees,exhibiting significant phase features regarding the rate and scale of transfers.Further investigation also demonstrated that the transfer of energy-intensive manufacturing industries has a U-shaped enabling effect on TFP in western China with the scale effect greater than the technology effect.Therefore,it is necessary to transition from“extensive industrial transfer”at the cost of the labor force,land,and resources to“modern industrial transfer”featured by technology and efficiency improvements to contribute to industrial restructuring in western China effectively.展开更多
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.展开更多
文摘Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.
基金partially supported by the Science and Technology Development Fund of Macao SAR(0050/2020/A1)。
文摘Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.
基金support provided in part by the National Key Research and Development Program of China under Grant 2020YFB1005804part by the National Natural Science Foundation of China under Grant 62372121,and in part by the NRPU 20-15516,HEC,Pakistan.
文摘Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers,manufacturers,suppliers,and consumers.Each stakeholder has implemented different technology-based systems to record and track product transactions.However,these systems work in silos,and there is no intra-system communication,leading to a lack of complete supply chain traceability for all apparel stakeholders.Moreover,apparel stakeholders are reluctant to share their business information with business competitors;thus,they involve third-party auditors to ensure the quality of the final product.Furthermore,the apparel manufacturing industry faces challenges with counterfeit products,making it difficult for consumers to determine the authenticity of the products.Therefore,in this paper,a trustworthy apparel product traceability framework called ChainApparel is developed using the Internet of Things(IoT)and blockchain to address these challenges of authenticity and traceability of apparel products.Specifically,multiple smart contracts are designed and developed for registration,process execution,audit,fault,and product traceability to authorize,validate,and trace every business transaction among the apparel stakeholders.Further,the real-time performance analysis of ChainApparel is carried out regarding transaction throughput and latency by deploying the compute nodes at different geographical locations using Hyperledger Fabric.The results conclude that ChainApparel accomplished significant performance under diverse workloads while ensuring complete traceability along the complex supply chain of the apparel industry.Thus,the ChainApparel framework helps make the apparel product more trustworthy and transparent in the market while safeguarding trust among the industry stakeholders.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the small Groups Project under grant number(168/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR59).
文摘Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.
文摘近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。
文摘Industrial Control Systems(ICS)and SCADA(Supervisory Control and Data Acquisition)systems play a critical role in the management and regulation of critical infrastructure.SCADA systems brings us closer to the real-time application world.All process and equipment control capability is typically provided by a Distributed Control System(DCS)in industries such as power stations,agricultural systems,chemical and water treatment plants.Instead of control through DCS,this paper proposes a SCADA and PLC(Programmable Logic Controller)system to control the ratio control division and the assembly line division inside the chemical plant.A specific design and implementation method for development of SCADA/PLC based real time ratio control and automated assembly line system in a chemical plant is introduced.The assembly line division is further divided into sorting stage,filling stage and the auxiliary stage,which includes the capping unit,labelling unit and then the storage.In the ratio control division,we have defined the levels inside the mixer and ratio of the raw materials through human machine interface(HMI)panel.The ratio of raw materials is kept constant on the basis of flow rates of wild stream and manipulated stream.There is a flexibility in defining new levels and the ratios of the raw materials inside the mixer.But here we taken the predefined levels(low,medium,high)and ratios(3:4,2:1,2:5).Control valves are used for regulating the flow of the compositions.In the assembly line division,the containers are sorted on the basis of size and type of material used i.e.,big sized metallic containers and small sized non-metallic containers by inductive and capacitive proximity sensors.All the processes are facilitated with laser beam type or reflective type sensors on the conveyor system.Building a highly stable and dependable PLC/SCADA system instead of Distributed Control System is required to achieve automatic management and control of chemical industry processes to reduce waste manpower and physical resources,as well as to improve worker safety.
文摘The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industrial transfer of energy-intensive manufacturing in the eastern,central,and western regions since the policy of large-scale development of western China was implemented.We measured the total factor productivity(TFP)of western China using the DEAMalmquist index method.We conducted a regression analysis to measure the effect of western China’s undertaking of the transfer of the energy-intensive manufacturing industry.The findings of this study show that during 2000–2019,eleven provinces(as well as autonomous regions and municipalities)in western China undertook the transfer of the energy-intensive manufacturing industry from the eastern and central regions to varying degrees,exhibiting significant phase features regarding the rate and scale of transfers.Further investigation also demonstrated that the transfer of energy-intensive manufacturing industries has a U-shaped enabling effect on TFP in western China with the scale effect greater than the technology effect.Therefore,it is necessary to transition from“extensive industrial transfer”at the cost of the labor force,land,and resources to“modern industrial transfer”featured by technology and efficiency improvements to contribute to industrial restructuring in western China effectively.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project under Grant No.(G:651-135-1443).
文摘Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
