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

In order to have a good visual analysis for the seabed, we created a 'virtual-seabed' according to the real one. The 3D seabed platform in this poster is based on a 'section-drilling' model that can accomplish the modeling, visualization and analysis of the integrated data of the seabed on either website or smartphone.

3D seabed modeling and visualization on ubiquitous context

A 3D Shenzhen city web platform based on WE-BVRGIS is presented. A 3D globe browser is employed to load all kinds of demanded data of the city, such as 3D building model data, residents information, real-time and historical traffic data. Using these data, the 3D analysis and visualization of the concerned city massive information are conducted in the platform. All the presented functions of the platform are extracted from the practical customer demand. The system design has considered some existing Geographic human-computer interaction (GeoHCI) research results.

XEarth: A 3D GIS platform for managing massive city information

To investigate the diversified technologies in Internet of Vehicles (IoV) under intelligent edge computing, artificial intelligence, intelligent edge computing, and IoV are combined. Also, it proposes an IoV model for intelligent edge computing task offloading and migration under the SDVN (Software Defined Vehicular Networks) architecture, that is, the JDE-VCO (Joint Delay and Energy-Vehicle Computational task Offloading) optimization. And the simulation is performed. The results show that in the analysis of the impact of different offloading strategies on the IoV, it is found that the JDE-VCO algorithm is superior to other schemes in terms of transmission delay and total offloading energy consumption. In the analysis of the impact of the task unloading of the IoV, the JDE-VCO algorithm is less than RTO (Random Tasks Offloading) and UTO (Uniform Tasks Offloading) algorithm schemes in terms of the number of tasks per unit time, and the average task completion time for the same amount of uploaded data. In the analysis of the packet loss ratio and transmission delay, it can be found that the packet loss ratio and transmission delay of the JDE-VCO algorithm are less than the RTO and UTO algorithms. Moreover, the packet loss ratio of the JDE-VCO algorithm is about 0.1, and the transmission delay is stable at 0.2s, which has obvious advantages. Therefore, through research, the IoV model of task offloading and migration built by intelligent edge computing can significantly improve the load sharing rate, offloading efficiency, packet loss ratio, and transmission delay when the IoV is processing tasks and uploading data. It provides experimental basis for the improvement of the IoV system.

Diversified Technologies in Internet of Vehicles Under Intelligent Edge Computing

The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources to circumvent collision issues. In this article, an improved RFID reader anticollision model is constructed by modifying the measure index, introducing a constraint function, and simultaneously considering collisions among readers and between readers and tags. The dense deployment of large numbers of readers increases the number of variables to be encoded, resulting in a high-dimensional problem that cannot be effectively and efficiently solved by traditional algorithms. Accordingly, distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) is proposed. The inertia weight and learning factors are adjusted during evolution, and an improved grouping strategy is presented. Moreover, various combinations of random number generation functions are tested. For improved efficiency, DPCCPSO is implemented with distributed parallelism. Experimental verification shows that the proposed novel algorithm exhibits superior performance to existing state-of-the-art algorithms, particularly when numerous RFID readers are deployed.

RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine &#x0028 DBN-SVM &#x0029 . Sliding window &#x0028 SW &#x0029 stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented. Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method &#x02BC s real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Zhihan Lv

Papers

3D seabed modeling and visualization on ubiquitous context

XEarth: A 3D GIS platform for managing massive city information

Diversified Technologies in Internet of Vehicles Under Intelligent Edge Computing

RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization

A real-time and ubiquitous network attack detection based on deep belief network and support vector machine