采煤机记忆截割自动化控制工作原理

为实现真正意义上的无人化、智能化开采,对记忆截割自动化控制技术进行了探讨。结合实际生产过程中的采煤工艺及割煤习惯,分析了编码器采集采煤机在工作面行走过程中具体位置的判断,以及行程传感器采集行走过程中的滚筒采高的方式,并详述了采煤机行走轨迹图的绘制过程。基于人工免疫法工作原理,论述了采煤机PLC对采集数据进行分析、筛选、填充的原理,及相对稳定的煤层中记忆割煤示范刀数据的形成过程;在工作面煤层变化情况下,可以通过监控中心远程干预调整,实现自动截割控制技术。

动物对医学科学的贡献

医学科学为人类的生存、繁衍,以及人类社会的发展作出了不可估量的贡献。然而在医学科学的发展中,多种动物却也无声无息地作出了贡献。 在祖国医学中,人们为了验证药效,验证药物毒性,早就进行了动物实验。如《论衡道虚》中记载:“致生息之物密器之中,覆盖其口,漆涂其隙,中外绝气,息不得泄,有顷死也。” 约在公元前400年时,国外名医希波克拉底就解剖动物作为参照,描绘出人体结构和器官。

Distance Concentration-Based Artificial Immune Algorithm

The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate.

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.

A decision hyper plane heuristic based artificial immune network classification algorithm

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

Immune response-based algorithm for optimization of dynamic environments

A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle.The dynamic process of artificial immune response with operators such as immune cloning,multi-scale variation and gradient-based diversity was modeled.Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens,a sigmoid model that can clearly describe clonal proliferation was proposed.In addition,with the introduction of multiple populations and multi-scale variation,the algorithm can well maintain the population diversity during the dynamic searching process.Unlike traditional artificial immune algorithms,which require randomly generated cells added to the current population to explore its fitness landscape,AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments.Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks.Preliminary experiments show that AIDE can maintain high population diversity during the search process,simultaneously can speed up the optimization.Thus,AIDE is useful for the optimization of dynamic environments.

A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

A hybrid algorithm to design the multi layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial immunology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore improves the probabiligy of training algorithm to converge to global optima. The applications of the neural network in the modulation style recognition of analog modulated rader signals demonstrate the good performance of the network.

A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit

Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.

A Control Algorithm Derived from Immune Principle

Artificial immune systems (AIS) are biologically inspired problem solvers having been successfully applied in many fields. A controller was designed according to the interactive mechanism between immune molecules. Multiform antigens were constructed and corresponding antibodies designed. The concept of “antibiotic” is presented, whose features and injection time as well as construction method discussed. Based on biological immune mechanism, some fuzzy rules are summarized and used in constructing the controller. The result shows that this controller is simple in structure and can be easily computed, so it is suitable for real time control. The control variable can change adaptively according to the error and its change tendency. Therefore the controller is very flexible and can be directly used in controlling some nonlinear plants. To test the validity of the algorithm, two simulation examples are given, one is linear, and the other is nonlinear. The Simulation results indicate that the control performance of this algorithm is better than that of the conventional PID.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

中国是现代免疫学的故乡

祖国医学不仅具有独特的理论体系和宝贵的医疗经验,而且还蕴藏着丰富的有关免疫学的理论和实践。从古代的医书中可以看到类似近代医学中的人工免疫法,虽然由于历史条件的限制,不可能那么完善,但从时间上看,这些论述与应用比之近代免疫学要早几个世纪。我国古代的医学巨著《内经》就提出“真气从之,精神内守,病安从来”“

Optimization of thread partitioning parameters in speculative multithreading based on artificial immune algorithm

Thread partition plays an important role in speculative multithreading （SpMT） for automatic parallelization of ir- regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap- plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold （DT）, lower limit of thread size （TSL）, upper limit of thread size （TSU）, lower limit of spawning distance （SDL）, and upper limit of spawning distance （SDU）. Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com- binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm （AIA） to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench- marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00% on a 4-core platform compared with a machine learning based approach, and 8.92% compared with a heuristics-based approach.