Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-e...Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-energy vehicles(NEVs)epitomize China’s upgrade towards premium,intelligent,and green manufacturing.展开更多
Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent...Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent air conditioner factory has been certified as an exceptional smart factory in Jinwan District,Zhuhai City,south China’s Guangdong Province.展开更多
Time series classification(TSC)has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the proces...Time series classification(TSC)has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory.However,it is still challenging for the efficiency and accuracy of classification due to complexity,multi-dimension of time series.This paper presents a new approach for time series classification based on convolutional neural networks(CNN).The proposed method contains three parts:short-time gap feature extraction,multi-scale local feature learning,and global feature learning.In the process of short-time gap feature extraction,large kernel filters are employed to extract the features within the short-time gap from the raw time series.Then,a multi-scale feature extraction technique is applied in the process of multi-scale local feature learning to obtain detailed representations.The global convolution operation with giant stride is to obtain a robust and global feature representation.The comprehension features used for classifying are a fusion of short time gap feature representations,local multi-scale feature representations,and global feature representations.To test the efficiency of the proposed method named multi-scale feature fusion convolutional neural networks(MSFFCNN),we designed,trained MSFFCNN on some public sensors,device,and simulated control time series data sets.The comparative studies indicate our proposed MSFFCNN outperforms other alternatives,and we also provided a detailed analysis of the proposed MSFFCNN.展开更多
Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning techn...Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.展开更多
This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-pl...This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.展开更多
The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing sy...The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing systems.In the context of Industry 4.0,smart factories leverage advanced technologies such as IoT,cloud computing,artificial intelligence(AI),and data analytics to enable real-time decision-making,predictive maintenance,and optimization of resources.This paper presents an overview of a smart factory architecture that combines IoT devices,cloud platforms,and data integration layers to create a flexible,scalable,and efficient manufacturing system.The proposed architecture is designed to enhance operational performance,energy efficiency,and resource management while ensuring system scalability and flexibility to meet changing market demands.Additionally,the paper discusses key optimization techniques,including edge computing,predictive maintenance,and cloud-based analytics,which contribute to the overall effectiveness of smart factory operations.The integration of these technologies promises to deliver significant improvements in productivity,cost savings,and sustainability in manufacturing environments.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessi...Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.展开更多
On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng...On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”展开更多
Xiaomi’s First Smart Home Appliance Factory Starts Operation in Wuhan Chinese tech firm Xiaomi launched its first smart home appliance factory in Wuhan,central China’s Hubei Province,on October 28,marking a major ex...Xiaomi’s First Smart Home Appliance Factory Starts Operation in Wuhan Chinese tech firm Xiaomi launched its first smart home appliance factory in Wuhan,central China’s Hubei Province,on October 28,marking a major expansion of the tech giant’s manufacturing footprint beyond smartphones and vehicles.展开更多
AUTONOMOUS buses gliding through the streets,satellites with flexible solar wings whizzing through space,smart electric vehicle factories running at full speed...In recent years,Beijing has seen the emergence of a lar...AUTONOMOUS buses gliding through the streets,satellites with flexible solar wings whizzing through space,smart electric vehicle factories running at full speed...In recent years,Beijing has seen the emergence of a large number of cutting-edge technology companies,confirming the city’s role as a melting pot of scientific innovation in China.展开更多
In the process of transition from agricultural society to industrial society,which started with the Industrial Revolution in England,the mechanization process experienced five different stages and in the last stage,wi...In the process of transition from agricultural society to industrial society,which started with the Industrial Revolution in England,the mechanization process experienced five different stages and in the last stage,with the development of computers,automation in production was achieved.While developments in a certain region or country of the world spread to other parts of the world with technological spread,technological revolutions also spread and paradigm changes occurred.With the development of information processing technologies,productivity has started to increase with the use of automation and robot technology in production.This process,which continued until the 2010s,is thought to lead to the formation of smart factories that can produce under the dominance of robots,after the new point reached in artificial intelligence and robot technology,and this development will further increase productivity in production.Intelligent robots working in the internet of things system will be able to work with greater power and longer periods than humans,and smart factories that are almost never shut down will emerge.In the transformation in this process,which is also called robonomics,changes in the theory of economy may occur and a new economic order may emerge.The question of why behind-the-scenes countries,such as Turkey,could not catch up with the leading ones,is another matter of discussion.However,in such periods of technological paradigm change,an opportunity arises for lagging countries for their economic development.On the other hand,we can say that Turkey will either be able to catch up with the technological level of developed countries by taking advantage of the opportunity,by means of a step-by-step technological development,or it will continue to stay among the countries that lag behind by missing the opportunity.展开更多
Considered as a top priority of industrial devel- opment, Industry 4.0 （or Industrie 4.0 as the German ver- sion） has being highlighted as the pursuit of both academy and practice in companies. In this paper, based ...Considered as a top priority of industrial devel- opment, Industry 4.0 （or Industrie 4.0 as the German ver- sion） has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.展开更多
Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based appro...Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SO<sub>x</sub> emissions, converter head temperature, NO<sub>x</sub> emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.展开更多
With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized ...With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.展开更多
Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the co...Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.展开更多
1.Humanoid robots perform tasks in the world's first large-scale,multi-task collaborating training session at a 5G smart factory of Chinese automaker ZEEKR,March 1,2025,Ningbo,Zhejiang Province(Photo by VCG)2.A tr...1.Humanoid robots perform tasks in the world's first large-scale,multi-task collaborating training session at a 5G smart factory of Chinese automaker ZEEKR,March 1,2025,Ningbo,Zhejiang Province(Photo by VCG)2.A tram carries tourists through the atrium of Super Wenheyou Night Market Complex,Changsha,Hunan Province,February 17,2025.The market is notable for its immersive multi-story 80s-themed restaurants,resembling the former Kowloon Walled City in Hong Kong(Photo by VCG)。展开更多
A petrochemical smart factory is a green,efficient, safe and sustainable modern factory that combines cutting-edge information and communication technology with petrochemical advanced technology and equipment. A Cyber...A petrochemical smart factory is a green,efficient, safe and sustainable modern factory that combines cutting-edge information and communication technology with petrochemical advanced technology and equipment. A Cyber-physical System(CPS) is the infrastructure of a petrochemical smart factory. Based on the future challenges of the petrochemical industry, this paper proposes the definition, connotation and framework of a petrochemical CPS and constructs a CPS system at the enterprise, unit and field levels, respectively. Furthermore,the paper provides theoretical support and implementation reference of a CPS in the petrochemical industry and other industries by investigating the construction practice of a multi-level CPS in the China Petrochemical Corporation(SINOPEC).展开更多
We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligen...We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making tech-niques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting-edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.展开更多
文摘Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-energy vehicles(NEVs)epitomize China’s upgrade towards premium,intelligent,and green manufacturing.
文摘Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent air conditioner factory has been certified as an exceptional smart factory in Jinwan District,Zhuhai City,south China’s Guangdong Province.
基金This work was supported by the Technology Innovation Program(20004205,The development of smart collaboration manufacturing innovation service platform in textile industry by producer-buyer B2B connection funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea)).
文摘Time series classification(TSC)has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory.However,it is still challenging for the efficiency and accuracy of classification due to complexity,multi-dimension of time series.This paper presents a new approach for time series classification based on convolutional neural networks(CNN).The proposed method contains three parts:short-time gap feature extraction,multi-scale local feature learning,and global feature learning.In the process of short-time gap feature extraction,large kernel filters are employed to extract the features within the short-time gap from the raw time series.Then,a multi-scale feature extraction technique is applied in the process of multi-scale local feature learning to obtain detailed representations.The global convolution operation with giant stride is to obtain a robust and global feature representation.The comprehension features used for classifying are a fusion of short time gap feature representations,local multi-scale feature representations,and global feature representations.To test the efficiency of the proposed method named multi-scale feature fusion convolutional neural networks(MSFFCNN),we designed,trained MSFFCNN on some public sensors,device,and simulated control time series data sets.The comparative studies indicate our proposed MSFFCNN outperforms other alternatives,and we also provided a detailed analysis of the proposed MSFFCNN.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.2021R1C1C1013133)funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)supported by the Soonchunhyang University Research Fund.
文摘Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.
基金National Natural Science Foundation of China(No.50875204)National Basic Research "973" Project(No.2011CB706805)
文摘This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.
文摘The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing systems.In the context of Industry 4.0,smart factories leverage advanced technologies such as IoT,cloud computing,artificial intelligence(AI),and data analytics to enable real-time decision-making,predictive maintenance,and optimization of resources.This paper presents an overview of a smart factory architecture that combines IoT devices,cloud platforms,and data integration layers to create a flexible,scalable,and efficient manufacturing system.The proposed architecture is designed to enhance operational performance,energy efficiency,and resource management while ensuring system scalability and flexibility to meet changing market demands.Additionally,the paper discusses key optimization techniques,including edge computing,predictive maintenance,and cloud-based analytics,which contribute to the overall effectiveness of smart factory operations.The integration of these technologies promises to deliver significant improvements in productivity,cost savings,and sustainability in manufacturing environments.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金funded by Changzhou Science and Technology Project(No.CZ20230025)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.XSJCX23_36).
文摘Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.
文摘On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”
文摘Xiaomi’s First Smart Home Appliance Factory Starts Operation in Wuhan Chinese tech firm Xiaomi launched its first smart home appliance factory in Wuhan,central China’s Hubei Province,on October 28,marking a major expansion of the tech giant’s manufacturing footprint beyond smartphones and vehicles.
文摘AUTONOMOUS buses gliding through the streets,satellites with flexible solar wings whizzing through space,smart electric vehicle factories running at full speed...In recent years,Beijing has seen the emergence of a large number of cutting-edge technology companies,confirming the city’s role as a melting pot of scientific innovation in China.
文摘In the process of transition from agricultural society to industrial society,which started with the Industrial Revolution in England,the mechanization process experienced five different stages and in the last stage,with the development of computers,automation in production was achieved.While developments in a certain region or country of the world spread to other parts of the world with technological spread,technological revolutions also spread and paradigm changes occurred.With the development of information processing technologies,productivity has started to increase with the use of automation and robot technology in production.This process,which continued until the 2010s,is thought to lead to the formation of smart factories that can produce under the dominance of robots,after the new point reached in artificial intelligence and robot technology,and this development will further increase productivity in production.Intelligent robots working in the internet of things system will be able to work with greater power and longer periods than humans,and smart factories that are almost never shut down will emerge.In the transformation in this process,which is also called robonomics,changes in the theory of economy may occur and a new economic order may emerge.The question of why behind-the-scenes countries,such as Turkey,could not catch up with the leading ones,is another matter of discussion.However,in such periods of technological paradigm change,an opportunity arises for lagging countries for their economic development.On the other hand,we can say that Turkey will either be able to catch up with the technological level of developed countries by taking advantage of the opportunity,by means of a step-by-step technological development,or it will continue to stay among the countries that lag behind by missing the opportunity.
文摘Considered as a top priority of industrial devel- opment, Industry 4.0 （or Industrie 4.0 as the German ver- sion） has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.
文摘Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SO<sub>x</sub> emissions, converter head temperature, NO<sub>x</sub> emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R319)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR44The authors are thankful to the Deanship of Scientific Research at Najran University for funding thiswork under theResearch Groups Funding program Grant Code(NU/RG/SERC/11/4).
文摘With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1A2C2011391)was supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806Development of security by design and security management technology in smart factory).
文摘Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.
文摘1.Humanoid robots perform tasks in the world's first large-scale,multi-task collaborating training session at a 5G smart factory of Chinese automaker ZEEKR,March 1,2025,Ningbo,Zhejiang Province(Photo by VCG)2.A tram carries tourists through the atrium of Super Wenheyou Night Market Complex,Changsha,Hunan Province,February 17,2025.The market is notable for its immersive multi-story 80s-themed restaurants,resembling the former Kowloon Walled City in Hong Kong(Photo by VCG)。
文摘A petrochemical smart factory is a green,efficient, safe and sustainable modern factory that combines cutting-edge information and communication technology with petrochemical advanced technology and equipment. A Cyber-physical System(CPS) is the infrastructure of a petrochemical smart factory. Based on the future challenges of the petrochemical industry, this paper proposes the definition, connotation and framework of a petrochemical CPS and constructs a CPS system at the enterprise, unit and field levels, respectively. Furthermore,the paper provides theoretical support and implementation reference of a CPS in the petrochemical industry and other industries by investigating the construction practice of a multi-level CPS in the China Petrochemical Corporation(SINOPEC).
文摘We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making tech-niques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting-edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.
