Rule Table Research Articles

Application of evolutionary algorithm in performance optimization of embedded network firewall

With the development of the network, the problem of network security is becoming increasingly serious. The importance of a firewall as the "first portal" to network security is obvious. In order to cope with a complex network environment, the firewall must formulate a large number of targeted rules to help it implement network security policies. Based on the characteristics of the cloud environment, this paper makes in-depth research on network security protection technology, and studies the network firewall system. The system implements unified configuration of firewall policy rules on the cloud management end and delivers them to the physical server where the virtual machine is located. Aiming at the characteristics of virtual machines sharing network resources through kernel bridges, a network threat model for virtual machines in a cloud environment is proposed. Through analysis of network security threats, a packet filtering firewall scheme based on kernel bridges is determined. Various conflict relationships between rules are defined, and a conflict detection algorithm is designed. Based on this, a rule order adjustment algorithm that does not destroy the original semantics of the rule table is proposed. Starting from the matching probability of the rules, simple rules are separated from the default rules according to the firewall logs, the relationships between these rules and the original rules are analyzed, and the rules are merged into new rules to evaluate the impact of these rules on the performance of the firewall. New rules are added to the firewall rule base to achieve linear firewall optimization. It can be seen from the experimental results that the optimization strategy proposed in this paper can effectively reduce the average number of matching rules and improve the performance of the firewall.

Hierarchical fuzzy control based on spatial posture for a support-tracked type in-pipe robot

Autonomous movement is important for the in-pipe robot. Because of the complex environment of the pipe, traditional control methods such as proportional–integral–derivative (PID) can not be used to implement autonomous movement for the support-tracked type in-pipe robot. A hierarchical fuzzy controller is proposed in this paper, which consists of fuzzy steering control and fuzzy posture control. The fuzzy steering control is utilized to control the robot’s turning movement in the elbow pipe, while the fuzzy posture control is used to adjust the posture of the robot in the straight pipe. The robot’s posture will periodically coincide after every 120°, when the robot rotates around its central axis. The symmetry is helpful to reduce the 12 × 7 × 7 three-dimensional fuzzy posture control rule table to five 7 × 7 two-dimensional fuzzy rule tables. A support-tracked in-pipe robot prototype is developed to verify the performance of the hierarchical fuzzy controller. Simulation and experimental results show that the robot with the controller could successfully pass the 45° and 90° elbows with a smaller change of posture compared to the case without the controller. As the robot with the controller could pass the elbow without obvious posture change, the proposed controller can be utilized to implement autonomous movement of an in-pipe robot. Video

Research on Key Technologies of Elders’ Exoskeleton Robot Assisted by Physical Exercise Based on Fuzzy PID Control

In the past, walking aids (such as crutches and walking aids) widely used by the elderly in physical exercise had relatively single functions, low intelligence, insufficient safety and comfort, and could not meet the needs of users for independent living. Therefore, it is necessary to apply robot technology to the field of traditional walking aid equipment and design a more intelligent, comfortable and safe walking aid robot system. The gait control of the exoskeleton robot assisted by physical exercise for the elderly is studied, and an algorithm based on fuzzy PID is proposed. Firstly, a fuzzy PID control algorithm for the exoskeleton robot assisted by physical exercise for the elderly is given. Secondly, membership functions are selected according to the parameters of the robot joint angle error and error variation. Finally, combined with practical operation experience, a fuzzy rule table is established, and online self-tuning of PID parameters is completed. The experimental results show that the fuzzy PID control method can make the exoskeleton robot move smoothly and realize the gait control of the robot initially.

Sensor-Fused Fuzzy Variable Structure Incremental Control for Partially Known Nonlinear Dynamic Systems and Application to an Outdoor Quadrotor

The fuzzy rules with two premise variables, the switching surface of subsystem i and its time derivative, and a consequent variable with zero-order incremental control of subsystem i , are established. Irrespective of the controlled systems and their input/output data, the proposed control is designed by only the consistence between the controlled system and its fuzzy rule table. Due to stochastic noise and a low-end computing unit, the sensor-fused extended Kalman filter (EKF) can satisfactorily estimate three-dimensional pose. Based on system input/output response, the five control parameters and the values of fuzzy rule table are suitably tuned to achieve a satisfactory tracking response. It is called the sensor-fused fuzzy variable structure incremental control. Based on the lower bound of system functions and the relative upper bound of estimated output error and system functions, the stability of the closed-loop system is verified by the Lyapunov stability theory. Finally, the application to the circular path tracking at specific altitude of an outdoor quadrotor with comparison between the normal and broken propellers validates the effectiveness and robustness of the proposed control.

Methodological Development of Mathematical Modeling Techniques in Health Care

The advancement of mathematical model has utilized for simulating the output of medical is a development area over medicine whereas the modeling can be mentioned with several activities namely simulation or decision analysis and predictive modeling. However, the traditional modeling technique utilized in planning of health service, assessment reports and its efficiency, financing about health care and assessment in budget impact, assessment in health economics, surveillance of infectious disease and other health care application. Therefore, the mathematical modelling is performed as a frequent and timely benefit in order to make rapid decision making while facing investigation with several issues like time elapsing, unusual and unethical particularly projected for future. This paper focused in applying the mathematical modeling to accomplish an optimal decision making in healthcare whereas this study discuss about the specific modeling concepts namely decision tree and fuzzified rule tables on evaluation of health economics and better service planning that my replicate the individual experience or patients cohorts.

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.

Application of fuzzy intelligent reasoning method in art color measurement

Under the influence of the COVID-19 epidemic situation, many countries have taken many measures to control the flow of people. The inability of people to gather makes art color measurement a problem. Color matching and coordination and color space conversion have always been the research focus of art color measurement. This paper studies a method of fuzzy intelligent reasoning in art color measurement. Based on case-based reasoning and rule-based reasoning, this method is an important self-learning and self-maintenance method in the field of artificial intelligence and expert system. On the basis of expounding the basic characteristics of color and color space, this paper designs the process principle of case-based reasoning and the process of rule-based reasoning. In this paper, case and rule knowledge representation method, case retrieval technology and rule conflict resolution strategy are established. Based on the above strategy, the color case base is established. In addition, the rule table is established by combining color-brewer with rule reasoning and referring to color-brewer. The rule table has a certain reference value for the application of intelligent reasoning method in the field of art color measurement under the influence of COVID-19 epidemic.

Autonomous line follower robot with fuzzy based hybrid controller

The autonomous navigation problem is currently a popular research topic encountered in many different areas, from smart cars to automated warehouse operations. Autonomous navigation of robotic systems is examined in two basic areas, with and without external supports. Even if the robot uses external supports (like GPS, road signs, ground surface lines, barcodes etc.) or positioning itself with internal sensors (like gyro, IMU, etc.), it independently needs a control method to follow the desired route. When the widely used control approaches in the literature are examined, PID and fuzzy-based approaches are frequently encountered. They both offer advantages and disadvantages. Within the scope of this study, the positive aspects of both of control approaches are utilized and the hybrid control approach for a robot that can autonomously follow the ground line presented as an external reference is mentioned. The success of the approach was tested comparatively on an exemplary mobile platform. The applied results obtained experimentally showed that a more efficient fuzzy logic-based hybrid control method could be developed. In addition, the effect of the number of conditions in the rule table on the success of the hybrid control technique is shown in the study. It has also been experimentally demonstrated that the success of hybrid control approach with a limited low power consuming ARM based processor that is suitable to control most of lightweight mobile platforms.

NARX Prediction-Based Parameters Online Tuning Method of Intelligent PID System

Control parameters of classical control system are expected to be online tuned and optimized by intelligent methods, in order to improve performance and help engineers reduce a lot of repetitive work in dangerous and harmful working environments. Main ideas and works of this paper are as follows:Firstly, change ratio based expert PID control method (EA-PID) is proposed to expand range of control parameters. Expert rule table (ERT) of expert PID control method (E-PID) is replaced by change ratio table (CRT) of EA-PID. Adjusted parameters of EA-PID are the results of multiplying change ratios in current adjusting cycle and control parameters in previous adjusting cycle. Secondly, NARX prediction-based NARX-E-PID and NARX-EA-PID are proposed. The NARX neural network is designed as a time series predictor to predict the output of the control system, then control parameters are adjusted according to the predicted output. Thirdly, comparative simulations of all the above methods are implemented to verify the improved effects. Finally, theoretical analysis is provided to ensure the stability of control systems. Effect are as follows: Firstly, comparative simulations verify that the improved methods have faster control speed, smaller steady-state error, less overshoot, and better ability of anti-interference. Secondly, theoretical analysis shows that the unstable control systems with adjusted parameters can be changed into a stable system by stability judgment in each adjusting cycle.

A Comprehensive Review on a PV Based System to Harvest Maximum Power

In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.

Cellular automata as convolutional neural networks

Deep-learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural networks encode and represent dynamical rules. We explore this problem in the context of cellular automata (CA), simple dynamical systems that are intrinsically discrete and thus difficult to analyze using standard tools from dynamical systems theory. We show that any CA may readily be represented using a convolutional neural network with a network-in-network architecture. This motivates the development of a general convolutional multilayer perceptron architecture, which we find can learn the dynamical rules for arbitrary CA when given videos of the CA as training data. In the limit of large network widths, we find that training dynamics are nearly identical across replicates, and that common patterns emerge in the structure of networks trained on different CA rulesets. We train ensembles of networks on randomly sampled CA, and we probe how the trained networks internally represent the CA rules using an information-theoretic technique based on distributions of layer activation patterns. We find that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA produce shallower representations-illustrating how the underlying complexity of the CA's rules influences the specificity of these internal representations. Our results suggest how the entropy of a physical process can affect its representation when learned by neural networks.

Data fusion based on‐line product quality evaluation of ternary cathode material cyber‐physical systems

In the manufacturing process of ternary cathode material, batching, mixing, loading, and sintering procedures play decisive roles in product quality, and data generated by these procedures are collected by the ternary cathode material cyber-physical system (TCM-CPS). However, the data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation of product quality is hard. In this study, a data fusion based on-line product quality evaluation method of TCM-CPS is proposed. Data features of each procedure are firstly extracted and fused by combining feature indices and kernel principal component analysis. Then, in order to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Moreover, the on-line evaluation of product performance is achieved by setting a rule table in accordance with production experience. Finally, an industrial application verifies the proposed method, of which the effective and accuracy can be acquired from results.

Bundle-Updatable SRAM-Based TCAM Design for OpenFlow-Compliant Packet Processor

Static random-access memory (SRAM)-based ternary content-addressable memories (TCAMs) emulate TCAM functions with high throughput at low cost. However, the implementation of SRAM-based TCAM as a rule table in network switches tends to prolong updating latency, which can cause a false packet routing. This brief proposes a novel low-latency bundle-updatable TCAM (BU-TCAM) scheme that uses binary tree-based prefix encoding (BPE) to support single- and multiple-rule updating in a software-defined networking/OpenFlow network. The proposed encoding method transforms the original ternary rule data into a binary code word and determines the range of overlap in SRAM addresses to facilitate updating. This greatly decreases latency in cases where multiple rules are required to update on an SRAM-based TCAM. We implemented an emulated $64\times 32$ -bit TCAM of the proposed design on a Xilinx ZC-706 field-programmable gate array. The proposed scheme reduced updating latency by 79.6%, compared with a conventional updating structure, which had only 9.8% and 23% increases in LUTs and registers overhead, respectively.

Implementation of Zhu - Takaoka Algorithm on Web-Based Smartphone Price Comparison Applications

This research is motivated by the rise of online buying and selling sites that provide products that are the same as the price differences at each site, from these problems, the authors provide an option by combining several online buying and selling service sites in one website by comparing smartphone prices with the brands provided namely Samsung, Asus, Sony, Lenovo, Nokia, Apple and Xiaomi. Some trusted online buying and selling sites are able to present a variety of information on consumer needs, one of which is its special price competition in the smartphone buying and selling competition which makes the prices in each site online buying and selling services vary even in the same product. Seeing the conditions that occur, where each item displayed in each site has different prices, then consumers are required to be observant in choosing the items sought. Consumers will choose manually by visiting a site that provides goods as a price comparison. Thus consumers will spend a lot of time just looking for price comparisons of one product offered by each site by accessing sites one by one The zhu-takaoka algorithm is one of the string matching algorithms that can do a word search with many shifts with an array of 2 (two) dimensions. The core process of the zhu-takaoka algorithm is to do a word search with the Right-to-left scan rule technique. Each shift in the word search process determines the shift values in the Good Suffix Shift Rule table and Bad-Charcter Shift Rule. With this algorithm, it is expected to facilitate the user in the search process price comparison.

Color identification for quadcopter flight control and object inspection

This study focuses on quadcopter flight control and image application. The proposed quadcopter can be applied to inspect rust or damage areas of ship hull. Real-time image identification with proper devices is also proposed. Proportional–integral–derivative control and fuzzy control are used in flight control design. Due to hardware limitation of the quadcopter, complicate control theory is avoid in controller design. A simple fuzzy control is used in this study. In sensor fusion, air pressure altimeter, Global Positioning System (GPS), ultrasonic sensor, and optical flow sensor are utilized to maintain altitude holding in outdoor and indoor environments. In color identification, Pixy (CMUcam5) camera is used to perform image processing and image tracking. Signals from different sensors are processed in the Arduino board. In flight simulation, pitch and roll angle commands are constructed in a rule table. Flight control can be performed by feedback tracking error, and software simulations for proportional–integral–derivative and fuzzy control are given.

Research of High Precision Humidity Fuzzy Control Algorithm of One Kind Humidity Verification Device

Aiming at different accuracy requirements of humidity in a variety of environment, a fuzzy control algorithm is applied to design a humidity fuzzy control system. Based on the quantification of humidity, a membership function, fuzzy rules and a rule table are established. The experimental results show that this method has high control accuracy, small overshoot and good stability, and achieves high-precision intelligent control of humidity.

Hiding Messages Based on DNA Sequence and Recombinant DNA Technique

Data hiding unnoticeably conceals message to prevent such message from being leaked to the existing media so as to ensure the confidentiality of the relevant information, which plays an important role in DNA cryptography. A data hiding model based on DNA sequence and recombinant DNA technique has been proposed to transmit information securely. According to the proposed model, a message can be hidden on a DNA sequence by using a single mapping rule table, and further concealed in DNA plasmid by recombinant DNA technique. The recombined DNA can be concealed in organisms and screened by selectable markers. This single mapping mechanism can improve the embedding capacity. The way to hide message in organisms is more flexible than that in micro-point of DNA. The relevant experimental results and security analysis show that the cracking probability of the model is negligible, which can ensure the security of information.

Research on comment target extracting in Chinese online shopping platform

Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.

Fast Packet Classification using Recursive Endpoint-Cutting and Bucket Compression on FPGA

Packet classification is one of the important functions in today's high-speed Internet routers. Many existing FPGA-based approaches can achieve a high throughput but cannot accommodate the memory required for large rule tables because on-chip memory in FPGA devices is limited. In this paper, we propose a high-throughput and low-cost pipelined architecture using a new recursive endpoint-cutting (REC) decision tree. In the software environment, REC needs only 5–66% of the memory needed in Efficuts for various rule tables. Since the rule buckets associated with leaf nodes in decision trees consume a large portion of total memory, a bucket compression scheme is also proposed to reduce rule duplication. Based on experimental results on Xilinx Virtex-5/6 FPGA, the block RAM required by REC is much less than the existing FPGA-based approaches. The proposed parallel and pipelined architecture can accommodate various tables of 20 K or more rules, in the FPGA devices containing 1.6 Mb block RAM. By using dual-ported memory, throughput of beyond 100 Gbps for 40-byte packets can be achieved. The proposed architecture outperforms most FPGA-based search engines for large and complex rule tables.

Efficient TCAM Rules Distribution Algorithms in Software-Defined Networking

In software-defined networking (SDN), network rules are installed in ternary content-addressable memory (TCAM). TCAM is a scarce and expensive resource which is a bottleneck for scaling SDN. Rule distribution is a strategy to solve the TCAM shortage problem, which decomposes a large table stored at network ingress into a number of smaller sub-tables and distributes them across network switches. Rule distribution includes two sub-problems, sub-table allocation, and table decomposition. Sub-table allocation is to guarantee that each flow path passes all the partitioned sub-tables and maximize the number of partitioned sub-tables (for reducing the size of sub-tables). Table decomposition problem is to partition a large rule table into a number of balanced sub-tables and reduce the rules overhead. In this paper, we propose a sub-table allocation algorithm and size-balancing sub-table partition algorithm. Simulation results show that the performance of both algorithms is better than previous works in terms of reducing the TCAM entries used in each switch.