数字栖居:理解人与数字技术交互关系的一种设想

随着人工智能技术的快速迭代,人类自身发展及生存境况经历着前所未有的挑战。为此,突破“以人为中心”的人与技术主客二元对立的观点去关注数字的人、数字的技术以及二者的关系显得尤为必要。以海德格尔的存在论和哈拉维的后人类主义思想为思辨原型,提出“数字栖居”这一数字技术与人的交互设想,它以身体-技术互嵌共生为关系基础,以移动交互界面为物质基础,以“此在”的数字存在为视域基础。它由这三个重要架构组成,认为数字技术已不再是纯粹的客观对象,而是内化为人类生存的有机组成部分。“数字栖居”的设想力图在延续后人类主义的思考路径上,积极而批判性地探讨一种人与技术的关系境况,提供一种理解未来社会技术形态的可能。

Fingerspelling Recognition by Hand Shape Using Higher-Order Local Auto-Correlation Features

The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using HLAC （higher-order local auto-correlation） features is proposed. Furthermore, in order to use HLAC features more effectively, the use of image processing techniques： reducing an image resolution, dividing an image, and image pre-processing techniques, is also proposed. The experimental results show that the proposed method is promising.

Reduction of distortion and improvement of efficiency for gridding of scattered gravity and magnetic data

This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human- machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.

3D structure parameterization design and modeling for irregular structure of stope bottom

Stope mining design is a very important and complicated task in daily production design and technical management of an underground mine.Based on workface technology and human-computer interaction technology,this study introduces a method of 3D parametric design for the irregular structure of stope bottoms,and focuses on solving technical problems in surface modeling of stope bottom structure.Optimization of the minimum span length algorithm(MSLA) and the shortest path search algorithm(SPSA) is conducted to solve the problem of contour-line based instant modeling of stope bottom structures,which makes possible the 3D parametric design for irregular structure of stope bottom.Implementation process and relevant methods of the proposed algorithms are also presented.Feasibility and reliability of the proposed modeling method are testified in a case study.In practice,the proposed 3 D parameterization design method for irregular structure stope bottom proves to be very helpful to precise 3D parametric design.This method is capable of contributing to improved efficiency and precision of stope design,and is worthy of promotion.

Face Detection from Four Captured Images Related to Intelligent Room for the Deaf

The intelligent environment needs Human-Computer Interactive technology （HCI） and a projector projects screen on wall in the intelligent environments. We propose the front-face detection from four captured images related to the intelligent room for the deaf. Our proposal purpose is that a deaf user faces wall displaying everywhere. system gets the images from four cameras, and detects the user region from a silhouette image using a different method, detects and cuts a motion body region from a different image, and cuts the vertexchest region from the cut body region image. The system attempts to find front-face using Haar-like feature, and selects a detected front-face image from the vertex-chest region. We estimate the front-face detection of recognition rate, which shows somewhat successfully.

Theoretical Research on Novel Data Mining Algorithm based on Fuzzy Clustering Theory and Deep Neural Network

With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.