Complete Shaking Force and Shaking Moment Balancing of Mechanisms Using a Moving Rigid Body

This paper addresses the mass balancing of mechanisms using a single rigid body („balancing body“). Firstly, the expressions of dynamic forces and moments acting on the machine frame, which are caused by arbitrary planar and spatial mechanisms are established. The general balancing conditions are then derived. By motion control of the balancing body, any resultant inertia forces and moments of several mechanisms can also be fully compensated. The desired motion of the balancing body is calculated in order that the sum of inertia forces and moments of the mechanisms and the balancing body is zero. Theoretically, the balancing body is possible to compensate the dynamic loads of the machine frame, even if several mechanisms with any structure are located in the machine frame. The balancing theory of planar mechanisms is presented in more detail. Finally, the proposed balancing method is illustrated by a numerical example, in which three components of dynamic loads caused by a planar mechanism in the steady state are given as the time-periodic functions. It can be shown that the proposed approach is an alternative to the conventional balancing methods and especially applicable in practice with piezoelectric actuators

Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH