Recently, the research team of Professor Liu Jinxing from the School of Computer Science of Qufu Normal University has made important progress in the field of artificial intelligence. The related research results are "n cplp: a novel approach for predicting microscopic-associated diseases with network consistency projection and label propagation", "predicting mirna-disease associations through deep auto encoder with multiple kernel learning" and "a new graph auto encoder-based consensus-guided model fo" Scrna-seq cell type detection ",published in IEEE transactions on cybernetics (IEEE TCYB) and IEEE transactions on neural networks and learning systems (IEEE TN NLS).Qufu Normal University is the first signatory of the paper, and Professor Liu Jinxing is the correspondent of the paper.

IEEE TCYB期刊是人工智能领域最具影响力的国际学术刊物之一，SCI一区，影响因子19.118，在Automation & Control Systems类别排序1/65，Computer Science, Cybernetics类别排序1/24。IEEE TNNLS期刊由美国电气和电子工程师协会创办，是人工智能顶级期刊之一，SCI一区，影响因子14.255，在Computer Science, Hardware&Architecture类别排序1/54。

A large number of clinical studies have confirmed that there is a close relationship between microorganisms and diseases. Therefore, it is particularly important to infer the relationship between potential microorganisms and diseases. Professor Liu Jinxing’s research team proposed a new method based on network consistency projection and label propagation to predict the relationship between microorganisms and diseases. Methods The medical thesaurus and 16S rRNA gene sequence were used to calculate the semantic similarity of diseases and the functional similarity of microorganisms, and the network projection scores of microbial space and disease space were obtained by increasing the network consistency projection. Finally, the disease-related microorganisms were accurately predicted by the tag propagation method.

The association between miRNA and diseases is an important part of preventing, diagnosing and treating complex diseases. Professor Liu Jinxing’s research team proposed a deep learning method to predict miRNA- disease association by using a deep automatic encoder with multi-core learning. Firstly, the multi-core learning method was applied to construct miRNA similarity network and disease similarity network respectively, then the integrated miRNA feature representation and disease feature representation were input into the deep automatic encoder, and finally the new miRNA- disease association was predicted by the reconstruction error method.

In order to make effective use of single cell data and better explore the heterogeneity among cells, Professor Liu Jinxing’s research team proposed a consensus guidance model based on graph automatic encoder. The model generates feature matrix through feature learning, and carries out similarity learning based on distance fusion method. The model can accurately identify key features and effectively save the internal structure of data, which improves the accuracy of cell type identification.

Professor Liu Jinxing is the head of the artificial intelligence research team of Computer College of Qufu Normal University. Focusing on the national strategy of "Healthy China" and the big scientific plan of "Precision Medicine", the team actively connects the two industries of "new generation information technology" and "medical care and health care" of the top ten industries in Shandong Province, aiming at the theory, algorithm and software related to the intelligent calculation of health big data, especially the disease genetics and clinical big data. The innovation team has undertaken 10 national natural science funds, with accumulated horizontal and vertical funds of more than 10 million yuan, and published more than 150 papers in high-level journals and academic conferences at home and abroad, including more than 50 SCI TOP papers.

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There is no doubt that smart factories are the core of the future manufacturing industry. But what exactly is a smart factory, what technology makes it work, and what makes it "smart"?

In short, smart factories are manufacturing facilities that rely heavily on digital technology to improve efficiency and productivity. This means integrating 3D printers, collaborative robots, 5G, artificial intelligence, SERP, cloud computing, edge computing and so on into the manufacturing process.

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With so many cutting-edge technologies, smart factories can run with speed, accuracy and flexibility that traditional factories can’t match. As the world increasingly relies on digital manufacturing processes, smart factories will only become more common–and more important.

Of course, there is no "magic bullet" when it comes to creating smart factories. On the contrary, numerous emerging technologies have gathered together, bringing the manufacturing industry into a new era. Here, we focus on some key technologies that are indispensable to realize this vision.

Understand the technologies used in smart factories.

3D printing

A smart factory is incomplete without a few 3D printers. These multifunctional machines can use digital design as a guide to manufacture parts and products from scratch. As they can produce items on demand, they are very suitable for prototyping, small batch manufacturing and even customized orders.

3D printing or additive manufacturing gives the technology an appropriate name and is creating a brand-new world for design engineers. It is very suitable for quickly creating functional prototypes and highly detailed end-use parts, and it opens the door to complex geometric structures that cannot be realized by any other means. Far from being a one-size-fits-all technology, 3D printing uses a variety of processes, technologies and materials, including stereo lithography, selective laser sintering, multi-jet fusion, multi-jet, fused deposition molding (FDM), vacuum casting and direct metal laser sintering, all of which can be used to quickly build parts within the scope of plastics and metals.

3D printing can help reshape the future of manufacturing and design. In the modern world, customers demand a higher level of product personalization and customization. Therefore, 3D printing can become a differentiation factor for manufacturers to plan their supply chain strategies. If you can quickly print the exact parts you need on demand, then why do you need to store a large number of complete parts and assemblies, which will reduce the demand for spare parts?

Cooperative robot

Cooperative robot or cooperative robot is another key component of intelligent factory. These physical multifunctional machines are designed to work with humans, making them ideal for tasks such as packaging and assembly. And because of its user-friendly interface, cooperative robots can be easily trained to perform new tasks as required.

Unlike traditional robots, collaborative robots can be deployed in a few weeks, so they can save money quickly. For manufacturers, other advantages of collaborative robots are that they are easy to set up and program, and flexible enough to move around the factory.

Collaborative robots have the potential to take over 3D jobs-dirty, dangerous and boring-many of which are still done by humans. By cooperating with collaborative robots, employees can focus their time on more fulfilling roles and reduce the time spent on ordinary roles, thus improving their overall happiness. This includes getting rid of night shift, which, as we all know, will have adverse effects on workers’ health.

5G

5G is the next generation wireless technology, which will completely change the manufacturing industry. This is because smart factories rely heavily on data, and 5G will provide the high-speed, low-latency connection needed to quickly and efficiently transmit large amounts of data.

5G can provide solutions to the biggest challenges of manufacturing industry, from achieving net zero and attracting future talents to maximizing uptime and accelerating product development. Next-generation mobile connectivity provides almost unlimited capabilities, and it is possible to completely change every stage of manufacturing operations. With so many potential applications, manufacturing is one of the industries that is expected to benefit most from the arrival of 5G.

It is about ten times faster than 5G and 4G, and the peak speed can reach more than 1gbps (gigabits per second). Moreover, unlike previous generations, 5G is not just an upgraded communication network. It was built from the ground up to provide a better user experience, support new deployment models and provide new services.

artificial intelligence

Artificial intelligence (AI) is another area where smart factories lead the trend. Artificial intelligence can be used for tasks such as predictive maintenance, quality control and material handling. With the continuous development of artificial intelligence technology, smart factories will only become more efficient and effective.

Artificial intelligence will see the combination of data collected from sensors, machines and people, and then apply it to algorithms aimed at optimizing operations or realizing lights-out manufacturing. Although we are still a long way from realizing this scenario, in many other use cases, manufacturers are rapidly adopting artificial intelligence and spanning the value chain of the whole industry. Use cases revolve around workplace safety, machine/building management, machine vision and network security.

Combining AI/ML with other technologies such as sensors, machines and human input will significantly improve operations and may lead to new forms of industry innovation and productivity.

Smart factory workshops are usually equipped with SERP system, which stands for "self-repair and error prevention". These systems use sensors to detect errors in real time, and then take corrective measures to prevent errors from happening again. This helps to reduce waste, improve quality and keep the production line running smoothly.

Enterprise resource planning (ERP)

In industries such as manufacturing where efficiency is king, ERP can change the rules of the game. ERP behavior is a method to centrally manage all aspects of facility operations and processes, and allows unprecedented visibility, coordination and management in different processes that make up manufacturing business-ultimately improving operational efficiency.

Considering the numerous processes that must happen every day to keep the facility running, ERP and manufacturing are natural partners. ERP can not only help inventory management, supply chain, maintenance and quality assurance, but also make these processes communicate with each other.

cloud computing

Cloud computing is a must for smart factories. This is because cloud-based systems provide the scalability and flexibility that smart factories need to keep agile. In addition, they can help reduce costs by eliminating the need for on-site hardware and software.

By providing computing services such as server, storage, database, network, software, analysis and intelligence on the Internet (or cloud), manufacturers can realize faster innovation, flexible resources and economies of scale. Cloud services usually adopt a pay-per-use model to help companies reduce operating costs, run infrastructure more efficiently, and expand as business needs change.

Besides cost and speed, other advantages of cloud services include the ability to deliver the right amount of IT resources from the right geographical location when needed; No need for complex on-site infrastructure, such as "shelving and stacking", hardware setup, software patching and other time-consuming IT management chores; Of course, improve reliability and security. In addition, many cloud computing services run on the global network of secure data centers, which are regularly upgraded to the latest generation of fast and efficient computing hardware.

Edge calculation

Edge computing is another important part of the smart factory problem. Edge computing systems are designed to process data locally rather than send it to the cloud, thus bringing enterprise applications closer to data sources such as IoT devices or local edge servers. This helps to shorten response time and reduce latency, which is essential for manufacturing applications. This approach to data from the source can bring powerful business advantages, such as faster insight, faster response time and better bandwidth availability.

Fundamentally, the data is processed and analyzed closer to the creation point. Edge computing can help unlock the potential of large amounts of data created by more powerful connected devices. This enhanced analytical capability in edge devices can drive innovation, improve quality and value, and provide near real-time insight and predictive analysis.

Digital twinning

VR Digital Twinning Demonstrated in AMRC Digital Operating Room

Digital twinning is essentially an auxiliary system of production facilities, which uses integrated simulation model to quickly find solutions when problems occur.

These simulations enable operators to get a glimpse of the core of physical assets, to determine the ideal operation workflow, and to quickly find clear options by using the current data flow to help system operators make decisions; There is no time and cost involved in testing theories in the real world.

These highly complex virtual models act as exact counterparts (or twins) of physical "things". Sensors connected to physical assets collect data that can be mapped to virtual models. Operators who view digital twins can see important information about the performance of physical things in the real world.

Digital twinning is an important tool, which can help engineers and operators understand not only the performance of products, but also their future performance. By analyzing the data from the connected sensors and combining with other information sources, we can make an accurate prediction.

Conclusion:

As you can see, many things need to be done to build a smart factory. But with the help of these cutting-edge technologies, intelligent factories can run with speed, accuracy and flexibility that traditional factories can’t match. As the world increasingly relies on digital manufacturing processes, smart factories will only become more common–and more important.