There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

This work intends to enhance the standard of rehabilitative care provided to patients by optimizing the medical automation monitoring system enabled by wearable computing by edge cloud and Internet of Things technology. First, the recent research literature on edge cloud and wearable computing devices is analyzed. Recent studies in virtual reality and automated medical rehabilitation are used to analyze and contrast various data fusion techniques in wearable sensors. Subsequently, an edge cloud model is constructed to enable real-time tracking of patients’ vital signs, allowing for timely assessment of their health status and rehabilitation progress. Then, a wearable device information monitoring rehabilitation system is established to provide effective rehabilitation treatment for stroke patients. The monitoring module of the rehabilitation system incorporates an edge computing terminal device, which models the virtual circuit for enhanced functionality. Based on the findings, when the number of output codes from the network structure is set to 1000, the average conversion energy of the dynamic conversion system is 140, the first conversion energy of the proposed model is 160, and the second conversion energy is 220. Regarding the rehabilitation treatment effectiveness, the system developed here demonstrates superior operational efficiency and delivers improved outcomes in rehabilitation treatment. This work serves as a practical reference for advancing the intelligent transformation of the medical service system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The goal of this work is to examine the viability of an automated virtual rehabilitation system for use in medical rehabilitation training for stroke, auto accident, and post-operative stroke rehabilitation. Combining the current research status of edge cloud and wearable devices reveals that the majority of existing rehabilitation training approaches are primarily reflected at the theoretical level, with only a few instances of practical applications highlighted. Therefore, this work presents a novel method for designing a wearable medical information monitoring system that operates on the edge cloud with the wearable device computing automation service as its foundation. It can track the patient’s vitals in real time to gauge the rehabilitation’s success. When compared to other cutting-edge rehabilitation options, the validated established edge cloud model clearly stands out as superior and can aid patients in achieving better outcomes from their rehabilitation treatment. The result demonstrates that the method is effective. In the future, it is planned to apply this rehabilitation system to more treatment cases and improve the overall design of the system. 

Edge-Cloud-Based Wearable Computing for Automation Empowered Virtual Rehabilitation

This is the preprint version of our paper on 2015 International Conference on Virtual Rehabilitation (ICVR2015). The purpose of this work is designing and implementing a rehabilitation software for dysphonic patients. Constant training is a key factor for this type of therapy. The patient can play the game as well as conduct the voice training simultaneously guided by therapists at clinic or exercise independently at home. The voice information can be recorded and extracted for evaluating the long-time rehabilitation progress.

Preprint Serious Game Based Dysphonic Rehabilitation Tool

Ocean waves provide a large amount of renewable energy, and Wave energy converter (WEC) can convert wave energy into electric en-ergy. This paper proposes a visualization platform for wave power generation. The platform can monitor various indicators of wave power generation in real time, combined with Long Short-Term Memory (LSTM) neural network to predict wave power and electric-ity consumption in real time and visualize monitoring data. The plat-form can intelligently allocate power generation equipment based on the power generation forecast data to achieve precise matching of power generation and power consumption, thereby improving overall power generation efficiency.

Zhihan Lv

Papers

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

Edge-Cloud-Based Wearable Computing for Automation Empowered Virtual Rehabilitation

Preprint Serious Game Based Dysphonic Rehabilitation Tool

Intelligence Visualization for Wave Energy Power Generation

A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins