Improving production efficiency is the foundation for the manufacturing industry to gain a firm foothold. The manufacturing industry achieves smart operations by introducing AI applications, automatically identifying abnormalities, or making adjustment suggestions, and assisting companies in achieving more accurate adjustments to machines and upgrading equipment. During the process, the traditional manufacturers are transformed into the smart manufacturers.
Artificial intelligence has brought in a new generation of robotics technology: Robotics 2.0. The principal challenge is the transformation from original manual programming methods to true autonomous learning. Faced with this challenge for innovation in AI robotics, how can Taiwan's manufacturing industry best seize the opportunity?
To realize the smart city of the future, it is indispensable to build a safer and more efficient traffic environment, and smart vehicles with networking and various advanced functions are the key.
With the rapid development of the automotive industry in smart driving, safety assistance, automotive electronics, and human-machine interface-related technologies, the integration of virtual and real superimposed displays, driver monitoring systems (DMS), interactive functions, Internet of Things (IoT) of the smart cockpit system have become the current trend.
The unlimited business opportunities brought by the space industry are highly valued by governments and enterprises of various countries. As the technology research and development of the global industry becomes more and more mature, the future will enter the new space era.
The IoT is becoming fragmented, and the market demand for customized measurement is increasing. Under the four conditions of product size and shape, high computing, networking capabilities, and sensor interface integration, measurement and IoT vendors will customize design, so this article will focus on a key application of customized measurement: smart sensor.
By embedding dynamic force sensors in the machine and matching them with analysis software, it provides an intelligent solution for the machine. Expand the scope of application, increase production capacity and reduce waste in the customer's manufacturing process, thereby reducing energy consumption and moving towards energy saving and waste reduction.
The development of smart sensors can be a high degree of integration of mechanical, electrical, software/firmware, circuit, and sensor knowledge.
AOI (Automated Optical Inspection) is a high-speed and high-precision optical image inspection system. It uses machine vision as the inspection standard technology to improve the shortcomings of traditional manual inspection using optical instruments. The application level includes research and development from high-tech industries, manufacturing quality control, and even national defense, people's livelihood, medical care, environmental protection, electric power, etc., but why do we need AI when we have AOI? Let's find out together.
With the rise of global environmental protection awareness, the improvement of process efficiency and pollution reduction of plastic products have become important issues for the rubber and plastic industry. In response to the shortage of workers and the reduction of personnel contact, mechanical automation and cloud services have become the new normal in the industry.
Industrial robot system integrators are located at the downstream application end of the robot industry chain, providing application solutions to end customers. It is responsible for the secondary development of industrial robot applications and the integration of peripheral automation equipment and is an important part of industrial robot automation applications.
The image recognition function of artificial intelligence (AI) is becoming more and more powerful. Face recognition, license plate recognition, and object recognition are not uncommon. In the fields of smart manufacturing and warehousing logistics, more and more manufacturers are beginning to introduce AI technology into more special applications.
In the new normal of future changes, whether it is the ICT industry, the supply chain, or even the industrial ecosystem, it is necessary to think about how to improve resilience to respond to environmental changes from a mid-to-long-term perspective. It will be important to improve the resilience of the supply chain from procurement to production. The key to future competitiveness.
Electric vehicles are only a small part of the future mobility trend. The future trend is not only electric vehicles, but also generates hidden business opportunities, and the opportunities are endless.
Data science is a complex process of extracting, integrating, and analyzing data, combining knowledge from computer science, mathematics, statistics, and related fields to help companies understand their customers, understand industry competition, and make relative decision-making.
Deep learning is a way of machine learning, by building a network, setting goals, and learning. Deep learning is not a panacea for artificial intelligence, it can only be designed for specific needs.
Breakthroughs in deep learning in recent years have come from the development of Convolutional Neural Networks (CNNs or ConvNets). It is the main force in the development of the deep neural network field, and it can even be more accurate than humans in image recognition.
With the development of technology and the gradual shortage of working manpower, more and more AI intelligent robots have been developed and officially applied in the workplace. How should human beings think and respond to such a wave of technology? Will jobs really be completely replaced?