AI Servo Monitor uses artificial intelligence to predict possible failures of the drive systems for FANUC servomotors and spindle motors. AI Servo Monitor, in conjunction with MT-LINKi through machine learning, analyzes the daily performance of machines equipped with FANUC CNCs. Daily data is displayed in intuitive graphs which allows users to easily monitor abnormalities on these machines. Artificial intelligence automatically creates a baseline model of the machine while running in a normal state. An "anomaly score" developed expresses a difference in the baseline model and the daily recorded values.

FANUC's AI Servo Monitor provides machine health data and analysis to maximize uptime. HOFFMAN ESTATES, Ill.–(BUSINESS WIRE)–Downtime is the enemy of profitability in manufacturing, which is why FANUC, a leading global automation solutions provider, has introduced a new Industrial Internet of Things (IIOT) software designed to prevent production problems before they happen. AI Servo Monitor uses artificial intelligence to predict possible failures of the drive systems for FANUC servomotors and spindle motors. AI Servo Monitor, in conjunction with MT-LINKi through machine learning, analyzes the daily performance of machines equipped with FANUC CNCs. Daily data is displayed in intuitive graphs which allows users to easily monitor abnormalities on these machines.

General Electric Company is an American multinational conglomerate incorporated in New York City and headquartered in Boston. As of 2018, the company operates through the following segments: aviation, healthcare, power, renewable energy, digital industry, additive manufacturing and venture capital and finance. If you have forgotten your password you can reset it here.

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This paper provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI, and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom designed data processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

Heart disease is the leading cause of death worldwide. Currently, 33% of cases are misdiagnosed, and approximately half of myocardial infarctions occur in people who are not predicted to be at risk. The use of Artificial Intelligence could reduce the chance of error, leading to possible earlier diagnoses, which could be the difference between life and death for some. The objective of this project was to develop an application for assisted heart disease diagnosis using Machine Learning (ML) and Deep Neural Network (DNN) algorithms. The dataset was provided from the Cleveland Clinic Foundation, and the models were built based on various optimization and hyper parametrization techniques including a Grid Search algorithm. The application, running on Flask, and utilizing Bootstrap was developed using the DNN, as it performed higher than the Random Forest ML model with a total accuracy rate of 92%.

Sears will begin selling its appliances on Amazon.com, The announcement Thursday sent shares of Sears soaring more than 18 percent at the opening bell. The tie-up with the internet behemoth could give shares of the storied retailer one of its biggest one-day percentage gains ever. Sears, which also owns Kmart, said that its Kenmore Smart appliances will be fully integrated with Amazon's Alexa, allowing users to control things like air conditioners through voice commands. "The launch of Kenmore products on Amazon.com will significantly expand the distribution and availability of the Kenmore brand in the U.S.," Chairman and CEO Edward Lampert said in a company release.

Sears will begin selling its appliances on Amazon.com, The announcement Thursday sent shares of Sears soaring more than 18% at the opening bell. The tie-up with the internet behemoth could give shares of the storied retailer one of its biggest one-day percentage gains ever. Sears, which also owns Kmart, said that its Kenmore Smart appliances will be fully integrated with Amazon's Alexa, allowing users to use voice commands for air conditioners and other controls. "The launch of Kenmore products on Amazon.com will significantly expand the distribution and availability of the Kenmore brand in the U.S.," Chairman and CEO Edward Lampert said in a company release. Sears has struggled with weak sales for years, and announced more store closings earlier this month, partly due to the emergence of Amazon and other Internet operators.

FILE - In this May 17, 2017 file photo, an Amazon Alexa device is switched on for a demonstration of its use in a ballpark suite before a Seattle Mariners baseball game in Seattle. Struggling retailer Sears is looking to get a hand from Amazon, announcing that it will start offering its Kenmore products on the online powerhouse's website. Sears, which runs Kmart and its namesake stores, said that Kenmore Smart appliances will also be fully integrated with Amazon's Alexa. This will allow consumers to control products, like Kenmore Smart air conditioners, by making a request to Alexa. Shares of Sears Holdings, based in Hoffman Estates, Illinois, surged more than 8 percent in Thursday, July 20, premarket trading.

Cluster-wise linear regression (CLR), a clustering problem intertwined with regression, is to find clusters of entities such that the overall sum of squared errors from regressions performed over these clusters is minimized, where each cluster may have different variances. We generalize the CLR problem by allowing each entity to have more than one observation, and refer to it as generalized CLR. We propose an exact mathematical programming based approach relying on column generation, a column generation based heuristic algorithm that clusters predefined groups of entities, a metaheuristic genetic algorithm with adapted Lloyd's algorithm for K-means clustering, a two-stage approach, and a modified algorithm of Sp{\"a}th \cite{Spath1979} for solving generalized CLR. We examine the performance of our algorithms on a stock keeping unit (SKU) clustering problem employed in forecasting halo and cannibalization effects in promotions using real-world retail data from a large supermarket chain. In the SKU clustering problem, the retailer needs to cluster SKUs based on their seasonal effects in response to promotions. The seasonal effects are the results of regressions with predictors being promotion mechanisms and seasonal dummies performed over clusters generated. We compare the performance of all proposed algorithms for the SKU problem with real-world and synthetic data.