-- Robotic non-destructive disassembly of mating parts remains challenging due to the need for flexible manipulation and the limited visibility of internal structures. This study presents an affordance-guided teleoperation system that enables intuitive human demonstrations for dual-arm fix-and-disassemble tasks for mating parts. The system visualizes feasible grasp poses and disassembly directions in a virtual environment, both derived from the object's geometry, to address occlusions and structural complexity. T o prevent excessive position tracking under load when following the affordance, we integrate a hybrid controller that combines position and impedance control into the teleoperated disassembly arm. Real-world experiments validate the effectiveness of the proposed system, showing improved task success rates and reduced object pose deviation.

The proportion of elderly people is increasing worldwide, particularly those living alone in Japan. As elderly people get older, their risks of physical disabilities and health issues increase. To automatically discover these issues at a low cost in daily life, sensor-based detection in a smart home is promising. As part of the effort towards early detection of abnormal behaviors, we propose a simulator-based detection systems for six typical anomalies: being semi-bedridden, being housebound, forgetting, wandering, fall while walking and fall while standing. Our detection system can be customized for various room layout, sensor arrangement and resident's characteristics by training detection classifiers using the simulator with the parameters fitted to individual cases. Considering that the six anomalies that our system detects have various occurrence durations, such as being housebound for weeks or lying still for seconds after a fall, the detection classifiers of our system produce anomaly labels depending on each anomaly's occurrence duration, e.g., housebound per day and falls per second. We propose a method that standardizes the processing of sensor data, and uses a simple detection approach. Although the validity depends on the realism of the simulation, numerical evaluations using sensor data that includes a variety of resident behavior patterns over nine years as test data show that (1) the methods for detecting wandering and falls are comparable to previous methods, and (2) the methods for detecting being semi-bedridden, being housebound, and forgetting achieve a sensitivity of over 0.9 with fewer than one false alarm every 50 days.

With the increase of the number of elderly people living alone around the world, there is a growing demand for sensor-based detection of anomalous behaviors. Although smart homes with ambient sensors could be useful for detecting such anomalies, there is a problem of lack of sufficient real data for developing detection algorithms. For coping with this problem, several sensor data simulators have been proposed, but they have not been able to model appropriately the long-term transitions and correlations between anomalies that exist in reality. In this paper, therefore, we propose a novel sensor data simulator that can model these factors in generation of sensor data. Anomalies considered in this study were classified into three types of \textit{state anomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. The simulator produces 10 years data in 100 min. including six anomalies, two for each type. Numerical evaluations show that this simulator is superior to the past simulators in the sense that it simulates well day-to-day variations of real data.

Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerfulplatforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach.

Our company mainly focuses on building services to facilitate a better understanding of what event is taking place in the environmental sound scene (e.g. The conference on INTERSPEECH, one of the biggest conferences on the science and technology of spoken language processing, was held at Songdo ConvensiA, in Incheon, South Korea, from Sep. 18 to 22, 2022. Integrating two previous series of conferences (EUROSPEECH and ICSLP), the first INTERSPEECH was held in 2000, in Beijing. Since then, INTERSPEECH has gained popularity and held the 23rd event this year, 2022. Despite a small discrepancy between our main focus and the conference theme, because our company primarily concentrates on nonverbal audio signals other than speech itself, many papers from INTERSPEECH have aided our research so far.

South Korean tech giant Samsung Electronics said Thursday its operating profit rose 53.3 percent in the fourth quarter of 2021, as record sales helped overcome pandemic-induced supply chain challenges. The world's biggest smartphone maker said its operating profit rose to 13.87 trillion won ($11.55 billion) for the October-December period in 2021, up from nine trillion won in the same quarter the previous year. Thanks to high memory chip prices and strong consumer demand, Samsung had its highest annual sales of 279.6 trillion won in 2021, an 18 percent jump from a year earlier, the company said in a regulatory filing. Samsung achieved "record sales thanks to competitive products, despite continuing uncertainty," the tech giant said in a statement, singling out solid demand for its premium smartphone lines. While the Covid-19 pandemic has wreaked havoc on the global economy, it has helped many tech companies boom.

Smart home applications have been growing and companies keep developing ideas to make these applications more appealing to customers. It is still an open market and a good environment for more creation and development. Our team is aiming to participate in that industry by developing new solutions and providing more convenience to users. Our project uses Hexabitz modules for our hardware circuit. Hexabitz has been developing various modules that can be integrated together to build a complete PCB (printed circuit board) that can be programmed and controlled using software platforms.

Edge AI is here to stay! Artificial intelligence (AI) is powering many real-world applications which we see in our daily lives. AI, once seen as an emerging technology, has now successfully penetrated into every industry (B2B & B2C) Banking, logistics, healthcare, defence, manufacturing, retail, automotive, consumer electronics. Smart Speaker like Echo, Google Nest, is one such example of Edge AI solutions in the consumer electronics sector. AI technology is powerful, and human-kind has set its eye on the path of harnessing its potential to the fullest. Intelligence brought to the device can be very useful and creative.

Less cleaning power than we'd expect at this price You can get the machine in white or graphite steel, and it's much less boxy than the average top-loader with pole agitator, showcasing sleek curves and a touchpad alongside its cycle selection knob for customizing wash cycles. Like most modern top-loaders, the WT7305CV comes with a Deep Fill feature, which fills up the tub significantly more than it would otherwise. While this may be helpful in certain circumstances--for example, if your clothes are completely covered in mud--on the whole adding more water actually results in your clothes getting less clean. As such, it's a feature that should be used sparingly, not left enabled as a default. The LG WT7305CV has a large 4.8 cu.

Purchases you make through our links may earn us a commission. Our colleague Marc Saltzman from USA TODAY is here to share some insight into how smart robots can help do your housework and make your life easier. Now that society is cautiously opening up ahead of the fall season, the last thing you want to do is more work around the house. Not to mention, you might be busy getting the kids ready for another school year – at home, in class, or a bit of both. Fortunately, technology can help, so you can focus on what matters.