A Noise Based Medical Elites Silence Model and Public Health Opinion Distortion in Social Networks

Under the impact of internet populism, internet violence, and other noises on the internet, medical elites, who have a professional background, did not intend to share their opinions on the internet. Thus, misinformation about health is increasingly prevalent. We roughly divided the users in social networks into ordinary users, medical elites, and super-influencers. In this paper, we propose a communication model of health information based on the improved Hegselmann-Krause (H-K) model. By conducting MATLAB-based simulation, the experimental results showed that network noise was an important factor that interfered with opinion propagation regarding health. The louder the noise is, the harder it is for health opinions within a group to reach a consensus. But even in a noisy environment, super-influencers could influence the overall cognition on public health in the social network fundamentally. When the super-influencers held positive opinions in public health, the medical elite keeping silent had a noise-tolerant effect on opinion communication in public health, and vice versa. Thus, three factors concerning noise control, the free information release of medical elites, and the positive position of super-influence are very important to form a virtuous information environment for public health.

Improving accuracy of cavitation severity recognition in axial piston pumps by denoising time-frequency images

Vibration signal is a good indicator of cavitation in axial piston pumps. Some vibration-based machine learning methods have been developed for recognizing the pump cavitation. However, their fault diagnostic performance is often unsatisfactory in industrial applications due to the sensitivity of the vibration signal to noise. In this paper, we presented an intelligent method to recognize the cavitation severity of an axial piston pump under noisy environment. First, we adopted short-time Fourier transformation to convert the raw vibration data into spectrograms that acted as input images of a modified LeNet-5 convolutional neural network (CNN). Second, we proposed a denoising method for the converted spectrograms based on frequency spectrum characteristics. Finally, we verified the proposed method on the dataset from a test rig of high-speed axial piston pump. The experimental results indicate that the denoising method significantly improves the diagnostic performance of the CNN model under noisy environment. For example, the accuracy rate of the cavitation recognition increases from 0.52 to 0.92 at SNR of 4 dB by the denoising method.

Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.

Lightweight and Anonymous Mutual Authentication Protocol for Edge IoT Nodes with Physical Unclonable Function

Internet of Things (IoT) has been widely used in many fields, bringing great convenience to people’s traditional work and life. IoT generates tremendous amounts of data at the edge of network. However, the security of data transmission is facing severe challenges. In particular, edge IoT nodes cannot run complex encryption operations due to their limited computing and storage resources. Therefore, edge IoT nodes are more susceptible to various security attacks. To this end, a lightweight mutual authentication and key agreement protocol is proposed to achieve the security of IoT nodes’ communication. The protocol uses the reverse fuzzy extractor to acclimatize to the noisy environment and introduces the supplementary subprotocol to enhance resistance to the desynchronization attack. It uses only lightweight cryptographic operations, such as hash function, XORs, and PUF. It only stores one pseudo-identity. The protocol is proven to be secure by rigid security analysis based on improved BAN logic. Performance analysis shows the proposed protocol has more comprehensive functions and incurs lower computation and communication cost when compared with similar protocols.

Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns

Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%.

Seismic solutions utilizing existing in-mine infrastructure for mineral exploration: A case study from Maseve platinum mine, South Africa

We demonstrate the application of seismic methods using in-mine infrastructure such as exploration tunnels to image platinum deposits and geologic structures using different acquisition configurations. In 2020, seismic experiments were conducted underground at the Maseve platinum mine in the Bushveld Complex of South Africa. These seismic experiments were part of the Advanced Orebody Knowledge project titled “Developing technologies that will be used to obtain information ahead of the mine face.” In these experiments, we recorded active and passive seismic data using surface nodal arrays and an in-mine seismic land streamer. We focus on analyzing only the in-mine active seismic portion of the survey. The tunnel seismic survey consisted of seven 2D profiles in exploration tunnels, located approximately 550 m below ground surface and a few meters above known platinum deposits. A careful data-processing approach was adopted to enhance high-quality reflections and suppress infrastructure-generated noise. Despite challenges presented by the in-mine noisy environment, we successfully imaged the platinum deposits with the aid of borehole data and geologic models. The results open opportunities to adapt surface-based geophysical instruments to address challenging in-mine environments for mineral exploration.