算法法人的理论证立及构成要素探析

人工智能、区块链等算法技术孕育着迥异于传统法人的新型组织关系,当下主要表现为人工智能开始取代自然智能获得实体的控制权,去中心化特点的分布式账本技术建构的自治组织排斥传统中心化组织机构。该类基于算法架构的实体应被认为独立的法人主体,即算法法人。此乃新技术产业发展所需;能够构筑我国制度优势,推动国家战略实现;方便国家监管,助推美好生活;域外法治实践亦可为镜鉴。算法法人的技术特性决定其构成要素分殊,除特定算法技术要素,应根据算法运行模式、特点及目的事业实现方式等,具体判定成员、财产是否系构成要素。算法法人无需传统法人组织机构,但尚需人类协助,有必要依其类型设置代表人、管理人及技术管理人,分别负责算法法人的对外事务、内部管理及技术事务。

区块链与人工智能组织体法人化理路探正

赋予区块链与人工智能组织体以法人地位,只能基于法人制度的基本常识:基于区块链技术的去中心化组织体与基于自动化决策技术的人工智能组织体,并不超出由社团、财团构成的法人组织类型谱系的解释范围,区块链与人工智能技术本身无法独立构成法人的组织基础;区块链与人工智能技术对传统组织体的技术加持,不仅不能成为赋予其法人人格时的政策考量优惠,反而由于为组织体规避国家与社会监管提供技术便利而成为法人设立过程中的重点审核对象;区块链与人工智能技术可导致有关法人治理效能的一定提升,但在传统法人权利能力结构之外不应也无法围绕算法技术本身确立所谓算法法人独立财产与独立责任。

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Linkage intensity learning approach with genetic algorithm for causality diagram

The causality diagram theory, which adopts graphical expression of knowledge and direct intensity of causality, overcomes some shortages in belief network and has evolved into a mixed causality diagram methodology for discrete and continuous variable. But to give linkage intensity of causality diagram is difficult, particularly in many working conditions in which sampling data are limited or noisy. The classic learning algorithm is hard to be adopted. We used genetic algorithm to learn linkage intensity from limited data. The simulation results demonstrate that this algorithm is more suitable than the classic algorithm in the condition of sample shortage such as space shuttle’s fault diagnoisis.

Fast Learning in Spiking Neural Networks by Learning Rate Adaptation

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.

Unknown constrained mechanisms operation based on dynamic interactive control

In order to operate various constrained mechanisms with assistive robot manipulators, an interactive control algorithm is proposed in this paper. This method decouples motion and force control in the constrained frame, and modifies the motion velocity online. Firstly, the constrained frame is determined online according to previous motion direction; then the selection matrix is adjusted dynamically, the constrained motion direction is chosen as the driving-axis. Consequently, the driving-axis and non-driving-axis are decoupled; finally, velocity control and impedance control are implied on above axes respectively. The selecting threshold for driving-axis is also varying dynamically to fit different constrained mechanism. Door-opening experiments are conducted to verify the performance of the proposed method.

Metaheuristic post-optimization of the NIST repository of covering arrays

Construction of Covering Arrays （CA） with minimum possible number of rows is challenging. Often the available CA have redundant combinatorial interaction that could be removed to reduce the number of rows. This paper addresses the problem of removing redundancy of CA using a metaheuristic post- optimization （MPO） approach. Our approach consists of three main components： a redundancy detector （RD）; a row reducer （RR）; and a missing-combinations reducer （MCR）. The MCR is a metaheuristic component implemented using a simulated annealing algorithm. MPO was instantiated with 21,964 CA taken from the National Institute of Standards and Technology （NIST） repository. It is a remarkable result that this instantiation of MPO has delivered 349 new upper bounds for these CA.

Reliability-based design optimization for flexible mechanism with particle swarm optimization and advanced extremum response surface method

To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.

GENETIC ALGORITHM IN REDUCTION OF NUMERICAL DISPERSION OF 3-D ADI-FDTD METHOD

A new method to reduce the numerical dispersion of the three-dimensional Alternating Di-rection Implicit Finite-Difference Time-Domain (3-D ADI-FDTD) method is proposed. Firstly,the numerical formulations of the 3-D ADI-FDTD method are modified with the artificial anisotropy,and the new numerical dispersion relation is derived. Secondly,the relative permittivity tensor of the artificial anisotropy can be obtained by the Adaptive Genetic Algorithm (AGA). In order to demon-strate the accuracy and efficiency of this new method,a monopole antenna is simulated as an exam-ple. And the numerical results and the computational requirements of the proposed method are com-pared with those of the conventional ADI-FDTD method and the measured data. In addition the re-duction of the numerical dispersion is investigated as the objective function of the AGA. It is found that this new method is accurate and efficient by choosing proper objective function.

Localization and navigation using a novel artificial landmark for indoor mobile robots

This paper presents a practical topological navigation system for indoor mobile robots, making use of a novel artificial landmark which is called MR code. This new kind of paper-made landmarks earl be easi- ly attached on the ceilings or on the walls, lmealization algorithms for the two cases are given respective- ly. A docking control algorithm is also described, which a robot employs to approach its current goal. A simple topological navigation algorithm is proposed. Experiment results show the effectiveness of the method in real environment.

Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.

Machine-learning-aided precise prediction of deletions with next-generation sequencing

When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.

Tools and Algorithm for Concept Analysis Using Medical Data

Drug taxonomy could be described as an inherent structure of different pharmaceutical componential drugs. Unfortunately, the literature does not always provide a clear path to define and classify adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to understanding different drugs and could aid in the comparison to results of past and future studies. In terms of building the drugs taxonomy, symptoms information were selected, clustered and adapted for this purpose. Finally, although national or international agreement on taxonomy for different drugs is a distant or unachievable goal, individual investigations and the literature as a whole will be improved by prospective, explicit classification of different drugs using this new pharmacy information system （PIS） and inclusion of the study＇s approach to classification in publications. The PIS allows user to find information quickly by following semantic connections that surround every drug linked to the subject. It provides quicker search, faster and more intuitive understanding of the focus. This research work can pretend to become a leading provider of encyclopedia service for scientists and educators, as well as attract the scientific community-universities, research and development groups.

Combined Approach for Nonuniformity Correction in Infrared Focal Plane Array

A new algorithm of nonuniformity correction for infrared focal plane array(IRFPA) is reported,which is a combined algorithm based on both the two-point correction and artificial neural networks correction. The combined algorithm is calibrated by two-point correction,and the calibrated correction coefficients are automatically modified by BP algorithm. So it is not only calibrated,but also real-time processed. In adaptive nonuniformity correction algorithm,the phenomena ghost artifact and target fade-out are avoided by edge extraction. In order to get intensified image,the modified median filters are adopted. The simulated data indicates the proposed scheme is an effective algorithm.

A new method for extracting domain terminology

This article proposes a new general, highly efficient algorithm for extracting domain terminologies. This domain-independent algorithm with multi-layers of filters is a hybrid of statistic-oriented and rule-oriented methods. Utilizing the features of domain terminologies and the characteristics that are unique to Chinese, this algorithm extracts domain terminologies by generating multi-word unit （MWU） candidates at first and then fihering the candidates through multi-strategies. Our test resuhs show that this algorithm is feasible and effective.

Navigation of Robot Using VFH＋ Algorithm

This paper describes a reactive navigation proposed for a differentially driven robot. The aim of the reactive navigation is to prescribe behavior to the robot based on actual sensor values that is collision-free. Analysis of reactive navigation methods shows that there is no reliable reactive collision-free method. However, method VFH＋ is suboptimal reactive navigation method for static environment. Original method was proposed for ultrasonic rangefinders. Nowadays, much more sophisticated sensors are available. That is why our modification is proposed for a laser rangefinder attached to indoor mobile robot. Results are presented as simulation in Matlab and also as experiments with real robot. Based on these experiments, it can be claimed that VFH＋ is very effective reactive navigation method for various sensors and environments and it can be modified for different requirements on robot behavior.