Multi-source brain computing with systematic fusion for smart health

With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social–cyber–physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion.     

数控机床智能化的主要技术特征

智能化作为数控机床最重要的发展趋势,其开发、运用正日益受到机床生产厂商和机床使用者的重视。数控机床的智能化不仅关系到所加工产品质量的优劣和效率的高低;更为重要的是,这一目标的实现是网络制造、敏捷制造、虚拟制造、并行工程等先进生产方式实施的基础。重点介绍了数控机床智能化的主要技术特征。     

OpenClinical.net: A platform for creating and sharing knowledge and promoting best practice in healthcare

It is well known that medical knowledge is growing so rapidly that it is difficult or impossible for healthcare professionals to keep up. More and more techniques for diagnosing and treating diseases are becoming available, yet new research findings and clinical practices are slow to spread. Information technology and the internet are providing important new ways of disseminating knowledge in healthcare as in many other domains. Knowledge engineering techniques for supporting decision-making and process management are also becoming available, and can be used to support busy clinicians, helping to ensure that their decisions are consistent with current knowledge and clinical procedures are carried out in a timely, efficient and safe way. The OpenClinical.net project is building on these techniques to demonstrate a new paradigm for disseminating knowledge and promoting best practice. The key idea is that much professional expertise can be modelled as computer-interpretable knowledge and used to assist decision-making, workflow management, communication and coordination of care and many other professional tasks. The central goal of OpenClinical.net is to demonstrate how this might be done at scale, through a form of “crowd sourcing”, in order to create and maintain a sharable knowledge base that is available in an open access and open source repository. This paper provides an overview of the project and a summary of progress to date.     

Signal processing and time series description: A Perspective of Computational Intelligence and Granular Computing

This study provides a general introduction to the principles, algorithms and practice of Computational Intelligence (CI) and elaborates on their use to signal processing and time series. In this setting, we discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets or Granular Computing, and evolutionary optimization), identify their focal points and stress an overall synergistic character, which ultimately gives rise to the highly synergistic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation.     

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.     

一种压力机智能检测控制系统的设计

本系统是将智能化检测控制系统与精密压力机结合的新一代机械压力机,由精密压力机、智能监测系统、多级智能控制系统等组成,是一种能对整机状态进行在线检测、故障预警和自适应控制的智能化成形加工设备,适用于各种冲压零件,尤其是引线框、变压器硅钢片等功能性精密冲压零件的生产,可以推广应用于建筑、汽车及电气和电子元器件等行业,提升相关行业的钣金成形生产装备水平。     

Behavioural Production: Autonomous Swarm-Constructed Architecture

Until now, parametric processes have largely been confined to the architectural and engineering phases of a building's design. What possibilities, however, do robotics and artificial intelligence programming open up for extending Parametricism's influence into the construction phase? Robert Stuart-Smith, a course master at the Architectural Association Design Research Laboratory (AADRL), explores how architecture might adopt autonomous swarm-construction techniques. He describes the research that he has carried out with colleagues and students employing flying multicopters or unmanned aerial vehicles (UAVs) to design and additively manufacture 3D-printed buildings onsite.     

多传感器信息融合回归分析法及集成化的研究

以开发研制的“智能型压力变送器”为例，对多传感器信息融合技术进行了较为详细的研究，通过对二传感器的线性回归分析法的实验分析表明，该方法实现了对不同量纲的数据融合，而且在数据处理方面由软件实现，具有很大的通用性；当温度不受其他参数影响时，采用多段温度补偿法，将进一步提高精度。还提出了采用集成技术实现多信息融合方案，它以模糊理论为基础。     

励磁调节器结构设计布局中干涉检验模型

研究励磁调节器结构设计布局中出现的干涉问题 ,提出三维干涉检验系统的总体结构。该系统包括用户接口管理系统、元系统、布局实体信息提取系统、数据库管理系统及八叉树干涉检验系统等五大模块。它以智能工程理论和面向对象思想为技术背景。通过对励磁调节器内部结构的设计进行静态检验和动态检验 ,阐明励磁调节器三维干涉检验模型建立的过程     

Concept optimization for mechanical product using Genetic Algorithm

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimizalion in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.