Tilt detection and servo control method for the holographic data storage system

Recently photopolymer with disk type substrate is selected as a media of the holographic data storage system. Because shape of the media is a disk, a disk tilt must be occur when the media rotates. When disk tilt occurs the angle between the reference beam and the media is changed by the tilt, thus the data can not be recorded with a right angle or another data page is retrieved. The holographic data storage system is very weak to the tilt disturbance by the Bragg effect, therefore it is necessary to detect the disk tilt angle and compensate it. In this study, we measure the amount of the tilt error using external photo detector and compensate it by rotating the angle of galvano mirror with our own algorithm.     

Design of tracking servo control system for holographic data storage

Holographic data storage is a strong candidate for the next generation archive storage device. However, disturbances such as external shock, vibration, flutter and eccentricity of the disk affects the holographic data storage device. Thus, tracking servo control is necessary in order to enhance the signal noise ratio and reduce the bit error rate and retrieve the data accurately. It is also inevitable to face track overlapping problem if there is no datum to apply servo control during the recording process. In this research, we suggest a method forming a holographic servo track to detect the tracking error signal continuously without using any additional laser diode or pre-format layer. The tracking servo controller is designed to improve the track following performance, and the validity of the proposed method is confirmed through experiments on our holographic data storage system.     

Real time servo control of the holographic data storage system with an additional servo beam

Holographic data storage is a next generation data storage device, which has high storage density, high data transfer rate and short access time. The device is very sensitive to disturbances due to its enormous storage density. In the disk type holographic data storage device deviation occurs successionally on the position of the data as the disk rotates. Therefore the spot where the probe beam is illuminated should directly be corrected in order to exactly retrieve the desired data. In this paper we suggest a method using an additional beam containing a servo pattern in order to detect the deviation of the media. We also propose a real time servo control method to compensate the affect of the unpredicted disturbance.     

Tracking servo method using reflective optical filter for holographic data storage system

Holographic data storage system is one of the next-generation data storage systems and is characterized by its high storage density and fast data transfer rate. However, holographic data storage systems are very sensitive to disturbances that affect the position of the media. Therefore, tracking servo control is needed to ensure a good performance of the system even if disturbances occur, such as eccentricity of the disk and external shock. In our previous researches on tracking servo methods, we used additional beams or recorded servo track images with data pages to record additional gratings with data pages. Therefore, the recording density may be reduced and the system may be complicated. In addition, the performance of the system may be compromised by cross-talk noise caused between the reference beams and additional beams. In this paper, we propose a tracking servo method using the residual beam, which is reflected by the reflective optical filter. This method does not require recording supplementary gratings or use additional beams, and only needs to record data pages. The residual beam is retrieved with desired retrieved beam by the reference beam and wasted during the retrieving process. We first constructed a holographic data storage system and designed a reflective optical filter to detect tracking error signals. After detecting the tracking error signals, a tracking servo controller using a lead-lag compensator was incorporated to reduce the tracking error signals. The performance of the designed controller was verified by simulated and experimental results. Finally, the performance of the tracking servo method was investigated by comparing the retrieved data images.     

An intelligent distortion-detection algorithm for restoring data in a holographic data storage system

In a holographic data storage system (HDSS), misalignment of the optical components causes data distortion, including barrel, pincushion, and rotation distortion. Because holographic data storage systems are very sensitive, misalignment inevitably results in data distortion. It is important to acquire accurate distortion-free data from a HDSS system. This paper proposes an intelligent distortion-detection algorithm for restoring data in HDSSs. We used a fuzzy system and subtractive clustering algorithm to generate fuzzy rules for the detection algorithm. The proposed algorithm determines a value indicating how distorted the data image is. Using this algorithm, it is possible to compensate for data distortion. A simulation performed well using barrel, pincushion and rotation images.     

基于遗传算法优化的舵机伺服系统模糊控制

针对舵机伺服系统中存在的非线性因素和工作环境的未知干扰,提出将基于遗传算法优化的模糊控制算法应用到液压舵机伺服控制系统中;该方法利用遗传算法对采用串联二进制编码的隶属函数参数进行联合优化,并将优化过的控制规则用于设计模糊控制器;运用Simulink对系统模型进行仿真;仿真结果表明:基于遗传算法优化的模糊控制器,在液压舵机伺服系统中,具有良好的控制效果和较强的鲁棒性,为舵机伺服系统的控制提供了一条新的思路。     

Suggest a format for intelligence control and structure of holographic data storage system

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy all of these conditions, however holographic data storage system (HDSS) can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanically actuating part therefore fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to reduce errors of binary data stored in HDSS, a new method for bit error reduction is suggested. Firstly, find fuzzy rule using test bed system for Element of Holographic Digital Data System and make fuzzy rule table using subtractive clustering algorithm and genetic algorithm and Reduce prior error element and recording digital data. Secondly, Reduce prior error element and recording digital data using the particle filter method. Finally, Recording ratio and reconstruction ratio show good performance and we suggest intelligence control method and filter method. Our format table include intelligence control algorithm and filter method.     

Tracking servo method for holographic data storage using discrete pre-patterns

Page-oriented holographic data storage (HDS) is very sensitive to disturbances that affect the position of the recording medium. Accordingly, a more precise tracking servo is required for the recording process, and is also crucial for achieving high storage density. A compensation method is therefore essential for HDS recording. In this paper, we suggest some discrete pre-patterns for the tracking servo used in the recording process. This method is motivated by a tracking servo technique for a hard disk drive. Firstly, in designing the pattern shape, HDS characteristics are taken into account. Secondly, track error signals are analyzed. Thirdly, the discrete pre-pattern intervals are determined according to the track tolerance. Lastly, the feasibility of the new method is analyzed via simulations and experiments.     

改进遗传算法在智能悬臂梁模糊控制的应用

模糊控制器的设计是模糊控制系统的核心,而模糊控制器设计的关键部分是模糊规则,模糊规则的好坏决定了模糊控制系统的控制效果.而一般模糊规则是通过专家经验获得的,存在很大的主观性的缺点,本文以智能悬臂梁结构为研究对象,设计了模糊控制器,改进了遗传算法,提出了使用改进遗传算法对模糊规则进行优化的方法,并给出了遗传编码、适应度函数的确定方法,最后利用Matlab/Simulink建立智能悬臂梁结构的仿真模型,对模糊规则优化前后的智能悬臂梁振动控制结果进行对比.仿真结果表明,优化后的模糊规则使智能悬臂梁的振动幅度显著缩小,而且振动衰减速度明显加快.     

An iterative learning approach to compensation for the servo track writing error in high track density disk drives

The servo tracks of hard disk drives are written at the time of manufacture with the equipment of servo track writer. The disk vibrations or head fluctuations during servo track writing process give rise to servo track writing error. The servo track writing error may cause some critical errors during data writing operation. In this paper, we propose a new correction algorithm for the servo track writing error based on the iterative learning control technique. The estimate of the servo track writing error is constructed from the position error signal and updated iteratively at each disk rotation. Then, the estimate of servo track writing error is used to correct the position error signal in a feed-forward manner. Our correction algorithm is robust to system model uncertainties, computationally quite simple, and has fast convergence rate. Furthermore, we give a rigorous analysis for the convergence of our correction algorithm. In order to demonstrate the practical use of our work, we present some experimental results using a commercially available hard disk drive.     

Evolutionary design and implementation of a hard disk drive servo control system

The ever increasing demand for higher storage capacity and smaller magnetic hard disk drives have driven the need of developing a high performance head positioning servo control system. To meet the challenge, this paper presents the design and real-time implementation of a robust two-degree-of-freedom servo system for physical 3.5-in. hard disk drive with single voice-coil-motor actuator using a multi-objective evolutionary algorithm toolbox. Besides the simplicity in controller structure, such an evolutionary servo control system is capable of meeting various performance specifications of hard disk drives in both the time and frequency domains. It is shown that the servo system optimally moves the magnetic head onto the desired track with minimal control effort, and keeps it on the track robustly against plant uncertainties or runout disturbances. Validation results of the evolutionary servo control system are compared with classical PID and RPT controllers, which show excellent closed-loop response and robustness in the face of practical perturbations in HDD.     

模糊数据挖掘和遗传算法在入侵检测中的应用

论述了数据挖掘和遗传算法在入侵检测中的应用，详细描述了模糊关联规则和模糊频繁序列挖掘，并进一步介绍了如何采用遗传算法优化模糊集合隶属函数，从而达到改善入侵检测系统性能的目的。     

一种基于磁盘介质的网络存储系统缓存

随着计算规模越来越大,网络存储系统应用领域越来越广泛,对网络存储系统I/O性能要求也越来越高.在存储系统高负载的情况下,采用低速介质在客户机和网络存储系统的I/O路径上作为数据缓存也变得具有实际的意义.设计并实现了一种基于磁盘介质的存储系统块一级的缓存原型D-Cache.采用两级结构对磁盘缓存进行管理,并提出了相应的基于块一级的两级缓存管理算法.该管理算法有效地解决了因磁盘介质响应速度慢而带来的磁盘缓存管理难题,并通过位图的使用消除了磁盘缓存写Miss时的Copy on Write开销.原型系统的测试结果表明,在存储服务器高负载的情况下,缓存系统能够有效地提高系统的整体性能.     

Genetic reinforcement learning through symbiotic evolution forfuzzy controller design

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.     

遗传算法在电液伺服系统分层模糊控制中的应用

针对模糊控制中规则数和可调参数多的问题,对遗传算法在分层模糊控制系统中的应用进行了研究,考虑遗传算法具有强大的全局搜索能力,提出了基于遗传算法的分层模糊控制的设计方案。该方案采用2个模糊控制器分层连接,即第一层采用基于遗传算法优化的模糊控制器,第二层采用典型的模糊控制器,从而大大减少了模糊规则和可调参数的个数,便于实时控制。电液伺服系统的实际仿真结果验证了该方案的有效性。     

The design of a fuzzy cascade controller for ball and beam system: A study in optimization with the use of parallel genetic algorithms

In this study, we introduce a design methodology for an optimized fuzzy cascade controller for ball and beam system by exploiting the use of hierarchical fair competition-based genetic algorithm (HFCGA). The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball and exhibits a number of interesting and challenging properties when considered from the control perspective. The position of ball is determined through the control of a servo motor. The displacement change of the position of ball requires the change of the angle of the beam which determines the position angle of a servo motor. Consequently, the variation of the position of the moving ball and the ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer (1st) controller and the inner (2nd) controller in a cascaded architecture. Auto-tuning of the parameters of the controller (viz. scaling factors) of each fuzzy controller is realized with the use of the HFCGA. The set-point value of the inner controller (the 2nd controller) corresponds to the position angle of a servo motor, and is given as a reference value which enters into the inner controller as the 2nd controller of the two cascaded controllers. HFCGA is a kind of a parallel genetic algorithm (PGA), which helps alleviate an effect of premature convergence being a potential shortcoming present in conventional genetic algorithms (GAs). A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy cascade controller and the conventional PD cascade controller whose design relied on the use of the serial genetic algorithms.     

基于量子遗传算法的模糊滑模控制

针对一类具有不确定性的非线性系统,提出了一种新的基于量子遗传算法的模糊滑模控制器的设计方法.将模糊控制与滑模控制相结合,利用滑模控制使系统跟踪误差进入边界层内;启用模糊控制替代切换控制,并在边界层上通过监督函数平滑控制作用.在滑动模态产生条件下,通过量子遗传算法优化模糊控制器的控制规则,有效地解决了模糊滑模控制中模糊控制规则的确定问题,从而削弱了系统的抖振,改善了控制器的控制性能.仿真结果表明了该方法的有效性.     

Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier. Our approach is mainly composed of two phases. First, a large number of candidate rules are generated for each class using fuzzy association rules mining, and they are pre-screened using two rule evaluation criteria in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the classes specified in IIDS: namely Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L-remote to local. During the next stage, boosting genetic algorithm is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. Boosting mechanism evaluates the weight of each data item to help the rule extraction mechanism focus more on data having relatively more weight, i.e., uncovered less by the rules extracted until the current iteration. Each extracted fuzzy rule is assigned a weight. Weighted fuzzy rules in each class are aggregated to find the vote of each class label for each data item.     

基于遗传算法和模糊逻辑的智能飞控技术研究

设计了基于遗传算法和模糊逻辑控制的智能飞行控制系统及采用论域自调整的模糊控制器，控制器以角度跟踪误差及其微分信号为输入来控制相应的气动舵面偏转，实现对该姿态的跟踪控制。文中给出了控制器输入输出的隶属函数，设计了相应的规则库。并在此基础上进一步利用遗传算法对模糊控制器进行优化设计，给出了遗传算法各个参数的选择原则。仿真结果表明，基于遗传算法和模糊逻辑的智能飞控系统具有良好的控制效果。     

基于一种FGA–ANFIS技术的DMFC温度建模和控制

为了提高直接甲醇燃料电池(DMFC)的发电性能,采用自适应神经模糊推理技术(FGA-ANFIS)对电池的工作温度进行建模与控制.首先,基于实验的输入输出数据建立了DMFC电堆温度的自适应神经模糊辨识模型,避开了DMFC电堆的内部复杂性.然后,将训练好的网络模型作为DMFC控制系统的参考模型,采用一种改进的模糊遗传算法对神经模糊控制器的参数和模糊规则进行自适应调整.最后,通过仿真.将所提出的算法与非线性PID和传统模糊算法进行比较,结果表明所设计的神经模糊控制器具有较好的性能.