Hairpin-based state machine and conformational addressing: Design and experiment

In this paper, we propose a new architecture for a multi-state DNA machine whose conformation of repeated hairpin structures changes sequentially in response to input oligomers. As an application of the machine, we also propose molecular memory in which the machine is used as a memory unit. Addressing in the memory is realized through state transitions of the machine. We then describe a method for designing DNA sequences of the machine, which exhaustively checks conformational changes of the machine by dividing its secondary structure into hairpin units. The method is based on the minimum free energy of the structure, the structure transition paths, and the total frequency of optimal and suboptimal structures. DNA sequences designed by the method were tested in a chemical experiment in which a machine consisting of two hairpins was actually constructed. As a result, we verified that the multi-state DNA machine realized the expected changes in its secondary structure.     

计算科学的新领域：DNA计算（Ⅲ）

DNA计算是应用分子生物技术进行计算的新方法.从理论上研究DNA计算方法，有利于推动理论计算科学的发展.本系列文章应用形式语言及自动机理论技术，系统地探讨了DNA分子的可计算性及其计算能力.本文主要介绍DNA剪接计算模型的文法结构和剪接计算方法，探讨了不同DNA剪接计算模型的计算能力，证明了所有图灵机可计算的函数理论上都可以通过DNA剪接计算模型来计算.     

Biomimetic Approach to Tacit Learning Based on Compound Control

The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.      

简单0-1规划问题的动态DNA折纸计算模型

DNA折纸是一种全新的DNA自组装方法。将一个由DNA折纸卡槽、双态DNA机器、DNA行走机器人组装而成的动态折纸应用于求解0-1规划问题。其中DNA折纸卡槽由1条M13脚手架链和202条钉书钉链折叠而成。双态DNA机器分为不修饰和修饰金纳米颗粒两种情况,对应于0-1规划问题约束变量的取值为0或者1。DNA折纸卡槽和DNA双态机器组装成折纸基底。DNA行走机器人是7条单链折叠成的带有粘性末端的DNA折纸。在链的驱动下,DNA行走机器人在折纸基底上顺时针旋转行走,每步旋转120°。DNA行走机器人每走两步,与折纸基底上的DNA双态机器进行链置换,接收修饰的金纳米颗粒。当整个动态行走过程结束,根据透射电镜下DNA行走机器人接收的金纳米颗粒的大小和个数来判断约束变量的取值是否为可行解。该计算模型采用模块化结构,DNA折纸卡槽、双态DNA机器、DNA行走机器人等折纸均单独设计,且采用透射电镜读解,因而提高了模型实现的可行性。     

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

支持向量机在DNA微阵列数据分析中的应用研究

基于DNA微阵列实验,可以同时观测成千上万个基因的表达水平,使得人们能够在基因组水平上以系统的、全局的观念去研究生命现象及本质。支持向量机作为一种新的机器学习方法,最近几年在生物信息学领域得到了广泛的研究,在许多情况下,支持向量机取得了优于或接近其它方法的性能。就支持向量机在DNA微阵列的应用做一综述。     

一种输电线路巡检机器人控制系统的设计与实现

介绍了一种超高压输电线路巡检机器人控制系统的设计与实现方法．根据巡检作业任务的要求，采用遥控与局部自主相结合的控制模式实现巡检机器人沿线行走及跨越障碍．设计了巡检机器人有限状态机模型，实现了机器人遥控与局部自主控制的有机结合．采用基于激光传感器定位的方法实现了巡检机器人的自主越障控制．实验结果表明，该机器人可实现沿线行走及自主跨越障碍，从而验证了控制系统设计的有效性与合理性．     

Utilizing cable winding and industrial robots to facilitate the manufacturing of electric machines

Cable wound electric machines are used mainly for high voltage and direct-drive applications. They can be found in areas such as wind power, hydropower, wave power and high-voltage motors. Compared to conventional winding techniques, cable winding includes fewer manufacturing steps and is therefore likely to be better suited for automated production. Automation of the cable winding production step is a crucial task in order to lower the manufacturing costs of these machines. This article presents a production method using industrial robots for automation of cable winding of electric machine stators. The concept presented is validated through computer simulations and full-scale winding experiments, including a constructed robot-held cable feeder tool prototype. A cable wound linear stator section of an Uppsala University Wave Energy Converter and its winding process is used as a reference in this article. From this example, it is shown that considerable production cycle time and manufacturing cost savings can be anticipated compared to manual winding. The suggested automation method is very flexible. It can be used for the production of cable wound stators with different shapes and sizes, for different cable dimensions and with different winding patterns.     

插入/切割DNA计算系统模型研究

为建立以载体分子为基础的DNA计算系统,根据目的基因和载体分子连接操作的特性构造一类DNA计算模型.该模型基于上下文的插入/删除模型,将线性分子拓展到环形质粒分上进行讨论.以剪接系统理论为基础,对在单一限制性内切酶作用下的插入/切割操作进行模拟.最后证明了这类操作的图灵机表达能力.     

Designing Stable Finite State Machine Behaviors Using Phase Plane Analysis and Variable Structure Control

This paper discusses how phase plane analysis can be used to describe the overall behavior of single and multiple autonomous robotic vehicles with finite state machine rules. The importance of this result is that we can begin to design provably stable group behaviors from a set of simple control laws and appropriate switching points with decentralized variable structure control. The ability to prove stable group behavior is especially important for applications such as locating military targets or land mines. In this paper, we demonstrate how phase plane analysis has been used to explain the behavior of a 16 cm³ autonomous line-tracking robot with four finite states. After which, the analysis is extended to include the design of a decentralized variable structure controller that guides multiple vehicles to a goal while avoiding each other.     

Hybrid learning concepts based on self-organizing neural networks for adaptive control of walking machines

Online learning of complex control behaviour of autonomous mobile robots like walking machines is one of the current research topics. In this article, a hybrid learning architecture based on reinforcement learning (RL) and self-organizing neural networks for online adaptivity is presented. The hybrid concept integrates different learning methods and task-oriented representations as well as available domain knowledge. The proposed concept is used for RL of control strategies on different control levels on a walking machine.     

Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art

In recent years, since researchers began to study on Unmanned Aerial Vehicles (UAVs), UAVs have been integrated into today's everyday life, including civilian area and military area. Many researchers have tried to make use of UAVs as an ideal platform for inspection, delivery, surveillance, and so on. In particular, machine learning has been applied to UAVs for autonomous flight that enables UAVs do designated task more efficiently. In this paper, we review the history and the classification of machine learning, and discuss the state-of-the-art machine learning that has been applied to UAVs for autonomous flight. We provide control strategies including parameter tuning, adaptive control for uncertain environment, and real-time path planning, and object recognition that have been described in the literature.     

Control of Walking in the Stick Insect: From Behavior and Physiology to Modeling

Classical engineering approaches to controlling a hexapod walker typically involve a central control instance that implements an abstract optimal gait pattern and relies on additional optimization criteria to generate reference signals for servocontrollers at all the joints. In contrast, the gait of the slow-walking stick insect apparently emerges from an extremely decentralized architecture with separate step pattern generators for each leg, a strong dependence on sensory feedback, and multiple, in part redundant, primarily local interactions among the step pattern generators. Thus, stepping and step coordination do not reflect an explicit specification based on a global optimization using a representation of the system and its environment; instead they emerge from a distributed system and from the complex interaction with the environment. A similarly decentralized control at the level of single leg joints also may explain the control of leg dynamics. Simulations show that negative feedback for control of body height and walking direction combined with positive feedback for generation of propulsion produce a simple, extremely decentralized system that can handle a wide variety of changes in the walking system and its environment. Thus, there is no need for a central controller implementing global optimization. Furthermore, physiological results indicate that the nervous system uses approximate algorithms to achieve the desired behavioral output rather than an explicit, exact solution of the problem. Simulations and implementation of these design principles are being used to test their utility for controlling six-legged walking machines.     

Experimental realization of dynamic walking of the biped humanoid robot KHR-2 using zero moment point feedback and inertial measurement

This paper describes a novel control algorithm for dynamic walking of biped humanoid robots. For the test platform, we developed KHR-2 (KAIST Humanoid Robot-2) according to our design philosophy. KHR-2 has many sensory devices analogous to human sensory organs which are particularly useful for biped walking control. First, for the biped walking motion, the motion control architecture is built and then an appropriate standard walking pattern is designed for the humanoid robots by observing the human walking process. Second, we define walking stages by dividing the walking cycle according to the characteristics of motions. Third, as a walking control strategy, three kinds of control schemes are established. The first scheme is a walking pattern control that modifies the walking pattern periodically based on the sensory information during each walking cycle. The second scheme is a real-time balance control using the sensory feedback. The third scheme is a predicted motion control based on a fast decision from the previous experimental data. In each control scheme, we design online controllers that are capable of maintaining the walking stability with the control objective by using force/torque sensors and an inertial sensor. Finally, we plan the application schedule of online controllers during a walking cycle according to the walking stages, accomplish the walking control algorithm and prove its effectiveness through experiments with KHR-2.     

Gender Recognition Based on Computer Vision System

Detecting human gender from complex background, illumination variations and objects under computer vision system is very difficult but important for an adaptive information service. In this paper, a preliminary design and some experimental results of gender recognition will be presented from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image (DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract its characteristics. The results show that the proposed method can adopt some characteristic values and the accuracy can reach up to 100% gender recognition rate under combining the horizontal added vertical feature and using a normal image size and test data when people are walking at a fixed angle. Meanwhile, it will be able to achieve over 80% rate within some allowed fault-tolerant angle range.     

Isothermal reactivating Whiplash PCR for locally programmable molecular computation

Whiplash PCR (WPCR; Hagiya et al., in Rubin H, Woods DH (eds) DNA based computers, vol III, pp 55–72. American Mathematical Society, Providence, RI, 1999) is a novel technique for autonomous molecular computation where a state machine is implemented with a single stranded DNA molecule and state transition is driven by polymerase and thermal cycles. The primary difference between WPCR computation and other forms of molecular computing is that the former is based on local, rather than global rules. This allows many (potentially distinct) WPCR machines to run in parallel. However, since each state transition requires a thermal cycle, multi-step WPCR machines are laborious and time-consuming, effectively limiting program execution to only a few steps. To date, no WPCR protocol has been developed which is both autocatalytic (self-executing) and isothermal (with no change in temperature). In this paper, we describe some isothermal and autocatalytic protocols that use a combination of strand displacement and DNA polymerization events. Our designs include (1) a protocol where transition rules cannot be reused in subsequent computing (2) a protocol where rules can be reused using an auxiliary strand displacement event but does not prevent back-hybridization (an event responsible for limiting the program execution to only a few state transitions before the machine stalls), (3) a reusable rule protocol that prevents back-hybridization. Furthermore, we show that the third machine which gets rid of thermal cycles and still prevents back-hybridization, is computationally equivalent to the original WPCR machine. We also compute the state transition likelihood and the corresponding rate in this protocol. Finally we present a DNA sequence design of a 3-state isothermal and reactivating WPCR machine along with an experimental verification plan.     

Digital copy milling—autonomous milling process control without an NC program

Conventional NC machine tools do not generally allow the change of cutting conditions such as depth of cut and stepover during machining operations, once they are given machining commands as NC programs. For that reason, the NC programs must be prepared adequately and verified in advance, which requires extensive time and effort. It is therefore necessary to develop functions to generate the cutter path autonomously and control the cutting conditions adaptively during machining to optimize the cutting process, maintain stable cutting, and avoid cutting trouble. This paper proposes a new architecture to realize autonomous control of the cutting process without using NC programs. A technique called digital copy milling is developed to control the NC machine tool in real time. The digital copy milling system can generate tool paths in real time, based on the principle of copy milling. In addition, a new control strategy is developed to control the cutting conditions adaptively. A prototype of an autonomous controller was implemented in a three-axis control machining center. Thereafter, experimental milling tests were carried out to verify the effectiveness of the proposed system. The cutter paths were generated autonomously by the digital copy milling system. Results show that the cutting depth and stepover can be changed during milling tests. Cutting conditions were controlled adaptively.     

Unravel four hairpins!

DNA machines consisting of consecutive hairpins, which we have previously described, have various potential applications in DNA computation. In the present study, a 288-base DNA machine containing four consecutive hairpins was successfully constructed by ligation and PCR. PAGE and fluorescence spectroscopy experiments verified that all four hairpins were successfully opened by four opener oligomers, and that hairpin opening was dependent on the proper openers added in the correct order. Quantitative analysis of the final results by fluorescence spectroscopy indicated that all four hairpins were open in about 1/4 to 1/3 of the DNA machines.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．