• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.276-285
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• 2008

Traffic monitoring method is mainly loop detector and piezo sensor. But this method is only detecting the number of vehicle. Monitoring traffic volume is not checking the number of vehicle but checking the length of access road, width of road, number of passing people,passing vehicle,delayed vehicle. The traffic signal control cycle is not fixed by only passing vehicle number but all related traffic proposal. This paper proposed selecting common characteristic out of each unrelated traffic proposal through Analytic Hierachy Process and this characteristic is applied to compose fuzzy sensor algorithm which find out new traffic volume concept of confusion degree. The accumulated delayed vehicle time is shorter in new fuzzy sensor algorithm applied by AHP than other traffic method

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.349-352
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• 2000

In case of sensor faults, they can be detected by examining the sensor output values and the typical values of the system. And then the types of the faults are recognized by the analysis of symptoms of faults. If necessary self-validating sensor values are synthesized according to the types of faults, and then they are used for the controller instead of the raw data. In this paper, fuzzy logic is introduced in SEVA sensors to improve the system performance. And then the method is applied to the control of a flexible link system with the sensor fault problems for exact positioning to show the applicability.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1009-1013
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• 2009

In this paper, a robust fault detection method for varying temperature based on fuzzy model is proposed. To develop a robust force estimation model, it needs temperature information because the output of force sensor is affected by a temperature variation. The nonlinear dynamic system, such as the parking force of the EPB (Electronic Parking Brake) system is necessary to have a higher order equation model. But, because of the calculation time, the higher order equation model is hard to be used in real application. In case of the lower order equation model, the result is not as accurate as acceptable. To solve this problem, the robust fuzzy model-based fault detection is developed. A proposed fault detection method for varying temperature is verified by HILS (hardware in the loop simulation).

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.7-12
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• 1992

This paper proposes a sensor-based path planning method which utilizes fuzzy logic and neural network for obstacle avoidance of a mobile robot in uncertain environments. In order to acquire the information about the environment around the mobile robot, the ultrasonic sensors mounted on the front of mobile robot are used. The neural network, whose inputs are preprocessed by ultrasonic sensor readings, informs the mobile robot of the situation of environment in which mobile robot is at the present instant. Then, according to the situation class, the fuzzy rules are fired to make a decision on the mobile robot action. In addition, this method can be implemented real time since the number of fuzzy rules used to avoid the obstacle is small. Fuzzy rules are constructed based on the human reasoning and tuned by iterative simulations. The effective of the proposed avoidance method is verified by a series of simulations.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.119-122
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• 2002

The paper proposed the intelligent inference method which keeps MV(Mobile vehicle) a little way off from men and makes it follow them using fuzzy controller Recognizing positions of MV and Men and distance between them was used to infer movement angle and speed of the MV with multi-ultrasonic sensor and USB camera The very important thing Is that the MV needs to obtain surrounding Information from the sensor and the camera, then It needs to represent those circumstances MV was controlled by inference from the speed and angle which are obtained from sensor and camera. Traveling simulation with a real MV was performed repeatedly to verify the usefulness of the fuzzy logic algorithm which was proposed in this paper. And a successful result of the experiment demonstrated the excellence of the fuzzy logic controller.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1692-1711
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• 2016

Energy is a scarce resource in wireless sensor networks (WSNs). A variety of clustering protocols for WSNs, such as the zone-based stable election protocol-enhanced (ZSEP-E), have been developed for energy optimization. The ZSEP-E is a heterogeneous zone-based clustering protocol that focuses on unbalanced energy consumption with parallel formation of clusters in zones and election of cluster heads (CHs). Most ZSEP-E research has assumed probabilistic election of CHs in the zones by considering the maximum residual energy of nodes. However, studies of the diverse CH election parameters are lacking. We investigated the performance of the ZSEP-E in such scenarios using a fuzzy logic approach based on three descriptors, i.e., energy, density, and the distance from the node to the base station. We proposed an efficient ZSEP-E scheme to adapt and elect CHs in zones using fuzzy variables and evaluated its performance for different energy levels in the zones.

• Advances in aircraft and spacecraft science
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• v.2 no.3
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• pp.263-273
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• 2015

Carbon nanotube strain sensors, or so called "fuzzy fiber" sensors have not yet been studied sufficiently. These sensors are composed of a bundle of fiberglass fibers coated with CNT through a thermal chemical vapor deposition process. The characteristics of these fuzzy fiber sensors differ from a conventional nanocomposite in that the CNTs are anchored to a substrate fiber and the CNTs have a preferential orientation due to this bonding to the substrate fiber. A numerical model was constructed to predict the strain response of a composite with embedded fuzzy fiber sensors in order to compare result with the experimental results obtained in an earlier study. A comparison of the numerical and experimental responses was conducted based on this work. The longitudinal sensor output from the model matches nearly perfectly with the experimental results. The transverse and off-axis tests follow the correct trends; however the magnitude of the output does not match well with the experimental data. An explanation of the disparity is proposed based on microstructural interactions between individual nanotubes within the sensor.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.245-249
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• 2012

In this paper, we propose a navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using an ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3671-3689
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• 2019

Software defined networking brings unique security risks such as control plane saturation attack while enhancing the performance of wireless sensor networks. The attack is a new type of distributed denial of service (DDoS) attack, which is easy to launch. However, it is difficult to detect and hard to defend. In response to this, the attack threat model is discussed firstly, and then a DDoS attack prevention extension, called FuzzyGuard, is proposed. In FuzzyGuard, a control network with both the protection of data flow and the convergence of attack flow is constructed in the data plane by using the idea of independent routing control flow. Then, the attack detection is implemented by fuzzy inference method to output the current security state of the network. Different probabilistic suppression modes are adopted subsequently to deal with the attack flow to cost-effectively reduce the impact of the attack on the network. The prototype is implemented on SDN-WISE and the simulation experiment is carried out. The evaluation results show that FuzzyGuard could effectively protect the normal forwarding of data flow in the attacked state and has a good defensive effect on the control plane saturation attack with lower resource requirements.