April 22, 2020 -- Siemens Healthineers has received clearance from the Food and Drug Administration (FDA) for its AIDAN artificial intelligence technologies on the Biograph family of positron emission tomography/computed tomography (PET/CT) systems, which includes the Biograph Horizon, Biograph mCT, and Biograph Vision. AIDAN is built on a foundation of patient-focused bed design and proprietary AI deep-learning technology to enable four new features – FlowMotion AI, OncoFreeze AI, PET FAST Workflow AI, and Multiparametric PET Suite AI. Siemens Healthineers PET/CT systems with AIDAN offer enhanced protection against cyber threats via syngo Security – a security package for general regulatory security rules that enables compliance with the Health Insurance and Accountability Act (HIPAA). FlowMotion AI Because each patient's body habitus and presentation of disease is different, tailoring PET/CT protocols to produce the highest-quality diagnostic imaging information possible for each patient can be difficult and time-consuming. The standard one-size-fits-all protocol lacks personalization and is often of suboptimal quality.
"The AI chip can perform the many calculations needed in just milliseconds," Thon explains. This type of chip is also known as "acceleration hardware" as a result. For the first demonstration, the researchers chose an application using an autonomous robot. The machine-learning algorithms and their implementation for the gripping process are the result of a collaboration between researchers from Corporate Technology in Berkeley and the University of California, Berkeley. The algorithm uses data from the 3D camera mounted on the robot arm to calculate the ideal points for grasping the target object.
NEC Corporation has announced that it will be collaborating with Siemens to provide artificial intelligence (AI) monitoring and analysis. In a press release, NEC said that the collaboration will provide a solution for manufacturing that connects MindSphere, the cloud-based, open IoT operating system from Siemens, and NEC's System Invariant Analysis Technology (SIAT). According to the agreement that was signed by the two companies, NEC will be joining the MindSphere Partner Program, which can provide NEC with access to specialised technical training and support from Siemens as well as a number of joint go-to-market capabilities. Last April, Siemens announced the availability of Mendix, its low-code enterprise app development platform, on its MindSphere system. The move focused on offering essential support that is needed to easily test, develop and deliver MindSphere applications.
Within the next three months, Honeywell will bring to market the world's most powerful quantum computer in terms of quantum volume, a measure of quantum capability that goes beyond the number of qubits. Quantum volume measures computational ability, indicating the relative complexity of a problem that can be solved by a quantum computer. Honeywell's quantum computer will have a quantum volume of at least 64. This is twice as much as the best current system. Honeywell has demonstrated its quantum charge coupled device (QCCD) architecture, a major technical breakthrough in accelerating quantum capability.
Honeywell, a specialist in control technologies for buildings, has today launched its new cloud-based machine learning (ML) solution focused on reducing buildings' energy consumption. Honeywell says the Forge Energy Optimisation solution studies a building's energy consumption patterns and automatically adjusts to optimal energy saving settings without compromising occupant comfort levels. "Buildings aren't static steel and concrete – they're dynamic ecosystems and their energy needs fluctuate based on ever-changing variables like weather and occupancy," says Honeywell Connected Buildings vice president and general manager David Trice. "By employing the latest self-learning algorithms coupled with autonomous control, we can help drive efficiencies and create more sustainable practices." The Forge Energy Optimisation solution autonomously optimises a building's internal set points every 15 minutes to evaluate whether a building's heating, ventilation and air conditioning (HVAC) system is running at peak efficiency, according to Honeywell.
Implementation of the "Internet of Things" in the modern world is gaining pace at breakneck speed. Society is moving away from standalone devices and entering the realm of inter-connectivity. With uses in different facets of life, such as personal gadgets, retail, electricity distribution and financial services, IoT is making its mark. One such application field of IoT is in Smart Homes, or more specifically in the Heating, Ventilation, and Air Conditioning industry (HVAC). According to a report by Zion Market Research, the global smart HVAC control market is expected to reach almost USD 28.3 billion by 2025 as compared to USD 8.3 billion in 2018.
One of the challenges facing the industry is overproduction. With AI, manufacturers can perform demand forecasting to reduce the emissions emanating from climate-controlled warehousing. In production, AI, supported by data on processes, can improve the efficiency of industrial control mechanisms, such as HVAC (heating, ventilation, and air-conditioning) systems. Besides, the technology can help to reduce food waste through demand forecasting, optimization of delivery routes, and improvements in refrigeration systems.
-- Faults in HV AC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Y et the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HV AC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. T o overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.
David Borst from the MindSphere Application Center at Siemens Mobility has taken a keen interest in the Bengaluru tests. "The better we understand traffic patterns, the better we'll be able to manage them," he explains. For instance, he points out that the ability to identify different vehicle types can be useful in urban planning. "Deep learning techniques can also be employed to detect accidents and automatically notify police and ambulance services. The technology could also capture visual evidence of traffic violations, extract vehicle numbers and automatically generate traffic tickets."