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To drive and control autonomous guided vehicle by the autonomous system, the landmark can be attached and reported the driving information. This landmark can notify the information by its color. Vision-AGV captured the landmark by the image and then they analyze the meaning of the landmark. To implement this concept, the system should be designed for detecting and recognizing the landmark. Therefore, this paper proposes the method to detect and recognize the color of the landmark. To detect the landmark, circular Hough transform is applied. This algorithm can detect the shape of the circle. However, it detects the circle which is not the landmark. To sort out the landmark, peak signal-to-noise ratio is calculated. This value represents the similarity between two images. So, we compare the circle and the reference for determining whether it is a landmark or not and recognizing the color of the landmark. The circle is extracted by the circular Hough transform, and the reference represents each color such as green, red, yellow. To compare two things by peak signal-to-noise ratio, we determine the color of landmark. As shown in experimental results, the proposed method can detect and recognize the color of landmark in case of the traffic light.

A lot of researches have been made on how to know the position of the mobile robot when it knows the initial position of the mobile robot. For the robot to be completely unmanned, the robot also needs to find out its own initial position. To do this, it is necessary to obtain enough data to estimate the initial position without previous data. In Simultaneous Localization and Mapping (SLAM), Light Detection and Ranging (LiDAR) is often used to obtain accurate map for mobile robot. In this case, it is difficult to find the initial position because there is little information at initial start-up. On the other hand, stereo camera has the advantage that it can acquire more information than LiDAR by acquiring spatial information with the same principle as the human eye. However, the obtained 3D spatial information has a disadvantage of low precision. The purpose of this paper is to form a 3D map to be used for finding the initial position by linking LRF information to compensate for the low accuracy of the 3D map made only by the stereo camera in the environment.

Targeted noninvasive stimulation selectively increases activity of the human hippocampal network during memory formation., Posterior-medial and anterior-temporal cortical networks interact with the hippocampus and are thought to distinctly support episodic memory. We causally tested this putative distinction by determining whether targeted noninvasive stimulation could selectively affect neural signals of memory formation within the posterior-medial network. Stimulation enhanced the posterior-medial network’s evoked response to stimuli during memory formation, and this activity increase was coherent throughout the network. In contrast, there was no increase in anterior-temporal network activity due to stimulation. In addition, control stimulation of an out-of-network prefrontal cortex location in a separate group of subjects did not influence memory-related activity in either network. The posterior-medial network is therefore a functional unit for memory processing that is distinct from the anterior-temporal network. These findings suggest that targeted stimulation can lead to network-specific increases in excitability during memory formation and hold promise for efforts to fine-tune network involvement in episodic memory via brain stimulation.

Somatosensation plays a critical role in the dexterous manipulation of objects, in emotional communication, and in the embodiment of our limbs. For upper-limb neuroprostheses to be adopted by prospective users, prosthetic limbs will thus need to provide sensory information about the position of the limb in space and about objects grasped in the hand. One approach to restoring touch and proprioception consists of electrically stimulating neurons in somatosensory cortex in the hopes of eliciting meaningful sensations to support the dexterous use of the hands, promote their embodiment, and perhaps even restore the affective dimension of touch. In this review, we discuss the importance of touch and proprioception in everyday life, then describe approaches to providing artificial somatosensory feedback through intracortical microstimulation (ICMS). We explore the importance of biomimicry – the elicitation of naturalistic patterns of neuronal activation – and that of adaptation – the brain’s ability to adapt to novel sensory input, and argue that both biomimicry and adaptation will play a critical role in the artificial restoration of somatosensation. We also propose that the documented re-organization that occurs after injury does not pose a significant obstacle to brain interfaces. While still at an early stage of development, sensory restoration is a critical step in transitioning upper-limb neuroprostheses from the laboratory to the clinic.

Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.

This paper proposes an active damping control method for a grid-side converter that has an LCL grid filter in the back-to-back converter. To remove the resonant frequency components produced by the LCL filter, it is necessary to measure the grid current. To do this, sensors must be added. However, it is not necessary to add sensors because the grid current is estimated by designing a suboptimal observer. In order to remove the nonlinearity and to gain fast response of control, both feedback linearization and sliding mode control are applied. The proposed method is verified through a simulation.

Kinect sensor has two output data which are produced from red green blue (RGB) sensor and depth sensor, it is called color image and depth map, respectively. Although this device’s prices are cheapest than the other devices for three-dimensional (3D) reconstruction, we need extra work for reconstruct a smooth 3D data and also have semantic meaning. It happened because the depth map, which has been produced from depth sensor usually have a coarse and empty value. Consequently, it can be make artifact and holes on the surface, when we reconstruct it to 3D directly. In this paper, we present a method for solving this problem by using implicit surface representation. The key idea for represent implicit surface is by using radial basis function (RBF) and to avoid the trivial solution that the implicit function is zero everywhere, we need to defined on-surface point and off-surface point. Based on our simulation results using captured face as an input, we can produce smooth 3D face and fill the holes on the 3D face surface, since RBF is good for interpolation and holes filling. Modified anisotropic diffusion is used to produced smoothed surface.

This contribution discusses the impact of the Great War on China in terms of the collapse of the nineteenth century imperial order constructed around the concept of "civilization," in which China had been subordinated. Conceptions to strengthen China, from reformist to revolutionary, that had developed under this old order were now put to the test through this period of geopolitical change. Particular examples covered range from the diplomat Wu Tingfang, the public intellectual and politician Liang Qichao, to the nationalist revolutionary Sun Yat-sen.

We present a simple method to watermark three-dimensional (3D) triangular meshes that have been generated from the depth data of the Kinect sensor. In contrast to previous methods, which maintain the shape of 3D triangular meshes and decide the embedding place, requiring calculations of vertices and their neighbors, our method is based on selecting one of the coordinate axes. To maintain shape, we use discrete wavelet transform and constant regularization. We know that the watermarking system needs the information to be embedded; we used a text to provide that information. We used geometry attacks such as rotation, scales, and translation, to test the performance of this watermarking system. Performance parameters in this paper include the vertices error rate (VER) and bit error rate (BER). The results from the VER and BER indicate that using a correction term before the extraction process makes our system robust to geometry attacks.

Some solutions to solve the class imbalance problem that is one of the representative difficulties in machine learning have been proposed. Among them, SMOTE algorithm is proposed recently to reduce the influence of the problem and shows its remarkable performance to solve real world problems. This paper proposes a novel kind of the SMOTE algorithm by combining the previous SMOTE algorithm and fuzzy logic to deal with uncertainties underlying learning samples. Also, fuzzy c-means clustering is used to assign membership degree to given samples efficiently. Moreover, the extended gap statistics is applied to select the optimal number of cluster. The proposed algorithm is evaluated on using several benchmark datasets and shows its good performance by combining support vector machine classifier.

This paper proposes the method to adjust brightness information by applying CIE L∗a∗b∗ color space and adaptive neuro-fuzzy inference system. The image which is already captured by vision sensor should be adjusted brightness to recognize objects in an image. In case of proper intensity of lights, the clarity of an image is good to recognize objects. However, in case of improper intensity of lights, the image has darkish regions. It will leads to reduce success of object recognition. To make up for this week point, we adjust the image, which is a darkish image, by controlling brightness information of an image. Brightness information can be represented by CIE L∗a∗b∗ color space. So based on CIE L∗a∗b∗ color space, adaptive neuro-fuzzy inference system is implemented as control function. Control function carries out adjusting of brightness information by dealing with the value of L component of CIE L∗a∗b∗ color space. L component describes brightness information of an image. The values which is calculated by adaptive neuro-fuzzy inference system is called the adjustment coefficient. Finally, the adjustment coefficient is added to L component for adjusting brightness information. To verify the propose method, we calculated color difference with respect to RGB and CIE L∗a∗b∗ color space. As experimental results, the propose method can reduce color difference and makes the target image will be similar with reference image under proper intensity of lights.

A camera has been widely used in practical fields with a diversity of purposes recently. There is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. Therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L∗a∗b∗ color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L∗a∗b∗ color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. Through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.

The change of appearance of the target object is one of important issue in visual tracking. It is because some factors such as camera motion, illumination change, motion change, occlusion, and size change are influenced to the object target during tracking. Recently, discriminative correlation filters (DCF) gave good results to handle these problems. Unfortunately, the DCF only works in the single-resolution features maps. In this paper, multi-scale color features are investigated to to solve this limitation. The multi-scale color features are implemented with correlation filter where it works in the continuous domain. In consequence of this reason, the implicit model of the target object is needed. So that, an interpolation is used to solve this problem. The output of this method is selected from the circular convolution response which has maximum value. Extensive experimental results on VOT2015 benchmark dataset which consists of 60 challenging videos show that the multi-scale color features based on correlation filter performs favorably against several state-of-the-art methods.

The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram- and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other’s drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses from the color-histogram- and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.

Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier.

Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection. For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size. In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking. Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map. The location of the target object is then predicted based on the maximum value of the optimized final response map. Further, a model update is used to overcome the change in appearance of the target object during tracking. A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols. The results are then provided based on their accuracy-robustness (AR) rank. Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness. In addition, the proposed method significantly improves the robustness of HOG-based features.

In order to evaluate the performance of arrow that manufactures through production process, it is used that personal experiences such as hunters who have been using bow and arrow for a long time, technicians who produces leisure and sports equipment, and experts related with this industries. Also, the intensity of arrow`s impact point which obtains from repeated shooting experiments is an important indicator for evaluating the performance of arrow. There are some ongoing researches for evaluating performance of arrow using intensity of the arrow`s impact point and the arrow`s flying image that obtained from high-speed camera. However, the research that deals with mutual relation between distribution of the arrow`s impact point and characteristics of the arrow (length, weight, spine, overlap, straightness) is not enough. Therefore, this paper suggests both the system that could describes the distribution of the arrow`s impact point into numerical representation and the correlation model between characteristics of arrow and impact points. The inputs of the model are characteristics of arrow (spine, straightness). And the output is MAD (mean absolute distance) of triangular shaped coordinates that could be obtained from 3 times repeated shooting by changing knock degree 120. The input-output data is collected for learning the correlation model, and ANN (artificial neural network) is used for implementing the model.

This paper presents positioning improvement of a laser navigation system (LNS) using the unscented Kalman filter (UKF) and fuzzy inference system (FIS) for an automatic guided vehicle (AGV). The existing AGVs mainly used a magnetic system or an inductive system as a guidance system. However, those systems have high initial facility cost and are difficult to maintain according to changes of environment, and it can drive only the designated path by sensors which are installed on. The laser guidance system is developed to solve these problems, but it also has limitations which are slow response time and low accuracy. Therefore, we propose a sensor fusion method for the AGV. The sensors used in this paper are encoders, a gyro and the LNS, and they are fused by the UKF and FIS. To analyze the performance of the proposed system, we designed a fork-type AGV for ourselves and performed the experiment that was repeated 10 times under the same working conditions. In experimental results, we verified that the proposed method could improve positioning accuracy of the LNS effectively. In addition, it was appropriate to apply a real AGV system for autonomous driving.

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

Episodic memory is thought to involve functional interactions of large-scale brain networks that dynamically reconfigure depending on task demands. Although the hippocampus and closely related structures have been implicated, little is known regarding how large-scale and distributed networks support different memory formation demands. We investigated patterns of interactions among distributed networks while human individuals formed item-context memories for two stimulus categories. Subjects studied object-scene and object-location associations in different fMRI sessions. Stimulus-responsive brain regions were organized based on their fMRI interconnectivity into networks and modules using probabilistic module-detection algorithms to maximize measurement of individual differences in modular structure. Although there was a great deal of consistency in the modular structure between object-scene and object-location memory formation, there were also significant differences. Interactions among functional modules predicted later memory accuracy, explaining substantial portions of variability in memory formation success. Increased interactivity of modules associated with internal thought and anti-correlation of these modules with those related to stimulus-evoked processing robustly predicted object-scene memory, whereas decreased interactivity of stimulus-evoked processing modules predicted object-location memory. Assessment of individual differences in network organization therefore allowed identification of distinct patterns of functional interactions that robustly predicted memory formation. This highlights large-scale brain network interactions for memory formation and indicates that although networks are largely robust to task demands, reconfiguration nonetheless occurs to support distinct memory formation demands.

We designed and implemented a fork-type automatic guided vehicle (AGV) with a laser guidance system. Most previous AGVs have used two types of guidance systems: magnetgyro and wire guidance. However, these guidance systems have high costs, are difficult to maintain with changes in the operating environment, and can drive only a pre-determined path with installed sensors. A laser guidance system was developed for addressing these issues, but limitations including slow response time and low accuracy remain. We present a laser guidance system and control system for AGVs with laser navigation. For analyzing the performance of the proposed system, we designed and built a fork-type AGV, and performed repetitions of our experiments under the same working conditions. The results show an average positioning error of 51.76 mm between the simulated driving path and the driving path of the actual fork-type AGV. Consequently, we verified that the proposed method is effective and suitable for use in actual AGVs.

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle’s position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

This paper proposes two real-time feedback control strategies based on hourly measurements of effluent NH4-N and NO X -N concentrations. Using modified sigmoid functions to decide the DO setpoint, a control structure similar to the cascade-type control loop was selected as the real-time feedback NH4-N control strategy. For the realtime feedback NO X -N control strategy, a proper external carbon dose flow rate could be calculated based on the estimated NO X -N concentration in anoxic reactor by using the empirical equation. A control system, which included two proposed control strategies, was developed and applied in the pilot-scale A2/O process. As a result, the effluent NH4-N and NO X -N concentrations were maintained stably lower than the target values of 3 and 5 mg/L, respectively. Moreover, because the manipulated variables for removing the NH4-N and NO X -N concentrations were divided in the control strategies, the two different control strategies could be successfully applied together in the A2/O process.

In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.

This paper presents a sensitivity optimization of a MEMS (microelectromechanical systems) gyroscope for a magnet-gyro system. The magnet-gyro system, which is a guidance system for a AGV (automatic or automated guided vehicle), uses a magnet positioning system and a yaw gyroscope. The magnet positioning system measures magnetism of a cylindrical magnet embedded on the floor, and AGV is guided by the motion direction angle calculated with the measured magnetism. If the magnet positioning system does not measure the magnetism, the AGV is guided by using angular velocity measured with the gyroscope. The gyroscope used for the magnet-gyro system is usually MEMS type. Because the MEMS gyroscope is made from the process technology in semiconductor device fabrication, it has small size, low-power and low price. However, the MEMS gyroscope has drift phenomenon caused by noise and calculation error. Precision ADC (analog to digital converter) and accurate sensitivity are needed to minimize the drift phenomenon. Therefore, this paper proposes the method of the sensitivity optimization of the MEMS gyroscope using DEAS (dynamic encoding algorithm for searches). For experiment, we used the AGV mounted with a laser navigation system which is able to measure accurate position of the AGV and compared result by the sensitivity value calculated by the proposed method with result by the sensitivity in specification of the MEMS gyroscope. In experimental results, we verified that the sensitivity value through the proposed method can calculate more accurate motion direction angle of the AGV.

The ability to characterize feature objects from radar readings is often limited by simply looking at their still frame reflectivity, differential reflectivity and differential phase data. In many cases, time-series study of these objects’" reflectivity profile is required to properly characterize features objects of interest. This paper introduces a novel technique to automatically track multiple 3D radar structures in C,Sband in real-time using Doppler radar and profile their characteristic reflectivity distribution in time series. The extraction of reflectivity profile from different radar cluster structures is done in three stages: 1. static frame (zone-linkage) clustering, 2. dynamic frame (evolutionlinkage) clustering and 3. characterization of clusters through time series profile of reflectivity distribution. The two clustering schemes proposed here are applied on composite multi-layers CAPPI (Constant Altitude Plan Position Indicator) radar data which covers altitude range of 0.25 to 10 km and an area spanning over hundreds of thousands km2. Discrete numerical simulations show the validity of the proposed technique and that fast and accurate profiling of time series reflectivity distribution for deformable 3D radar structures is achievable.

Human eyes acquire numerous information and analyze an environment from day to day. Vision sensor is similar to that of human eye. One of research areas of vision sensor is driving control. The role of vision sensor is essential to control driving by controlling system such as automatic car and autonomous guided vehicle not human. Color recognition of the system such as automatic car is important so as to analyze a road sign like as traffic lights. RGB, which consists of red, green and blue, comes generally up when making a division of color in real world. However, it is difficult to recognize color by only RGB information. To recognize color landmarks for driving control, this paper takes RGB and CIE LAB color space into account. By composing these color system, color is determined by fuzzy inference system which is familiar to the way of human thinking. Circular Hough transformation helps to reduce data of an image effectively by extracting region of interest. In sequence, find the highest values about each of distribution maps. The highest values are converted to CIE LAB color space. To analyze and determine color of ROI, fuzzy inference system carry out by inputs which are original R, G, B components and a, b components.

A weather radar is an essential device which covers a wide range of observation area with remarkable performance. However, the observation results of the weather radar contain a various kinds of non-meteorological phenomena. One of the reasons disturbing weather forecasting process is interference patterns, such as RF interference echo and sun strobe echo due to delicate specification of radar receiver and interferences by wireless devices or solar energy, so called line-shaped echo in this paper. The line-shaped echo gives cause for decreasing performance and accuracy of weather forecasting result. Therefore, we suggest an interval type-2 fuzzy k-nearest neighbors to deal with underlying uncertainties in feature space of the line-shaped echo and obtain better classification results. For evaluating the proposed method, it is applied actual line-shaped echo cases. And the result proves that the method can distinguish the line-shaped echo from actual radar data successfully.

One of issue in generative approach for visual tracking is relates to computation time. It is because generative approach uses particle filter for modeling the motion and as a method to predict the state in the current frame. The system will be more accurate but slower computation if many particles are used. Recently, the combination between particle filter and sparse model is proposed to handle appearance variations and occlusion in visual tracking. Unfortunately, the issue about computation time still remains. This paper presents fast method for sparse generative approach in visual tracking. In this method, l1 minimization is used to calculate sparse coefficient vector for each candidate sample. Then, the maximum weighted is selected to represent the result. Based on simulations, our proposed method demonstrate good result in area under curve parameter and achieve four times faster than other methods with only use fifty particles.

In order to produce reliable weather forecasts, it is essential to discriminate non-meteorological targets from rain clouds in weather radar data. Identification of chaff echoes, which is one of the main noise sources, is uncertain and imprecise for skilled weather experts because characteristics of them are similar to those of precipitation echoes. This paper uses tree-initialized fuzzy classifier (FC) to identify chaff echoes. Fuzzy models have been widely applied to the domain of uncertainty and vagueness. Classification and regression tree is used to generate an initial crisp model (a set of crisp rules). The number of the rules, corresponding to complexity of the model, is systematically determined by performance criterion. Finally, after transforming the crisp model to the fuzzy one straightforwardly, parameters of the FCs are optimized by genetic algorithms. FCs have more flexible decision boundaries than binary decision trees with rectangular partitioning. In order to evaluate identification performance, the FCs, and comparison methods are applied to many cases where both chaff and non-chaff echoes occurred simultaneously. The results of experiments show that the FCs achieve the best identification performance.

[Purpose] The purpose of this study was to determine the correlation between the Berg Balance Scale (BBS) and acceleration of postural sway in the Clinical Test of Sensory Interaction and Balance (CTSIB) by using a triaxial accelerometer for quantitative assessment. [Subjects and Methods] Twenty-seven stroke patients participated in this study. Balance ability was evaluated with the BBS, and postural sway was evaluated with a triaxial accelerometer. The data were then analyzed for frequency and correlation by using statistical software (SPSS 18.0). [Result] Acceleration in left-right and forward-backward directions in all conditions of the CTSIB assessment showed a significant correlation with BBS assessment. Acceleration in Signal Vector Magnitude values in condition 3 of the CTSIB assessment showed a significant correlation with BBS assessment. [Conclusion] This study revealed that postural sway represented balance ability as acceleration in the quantitative measurement of kinematic analysis. This finding suggests that the triaxial accelerometer could be used as a measurement tool in clinical conditions.

This paper presents performance comparison of probabilistic methods based correction algorithms for localization of AGV (Autonomous Guided Vehicle). Wireless guidance systems among the various guidance systems guides the AGV using position information from localization sensors. Laser navigation is mostly used to the AGV of a wireless type, however the performance of the laser navigation is influenced by a slow response time, big error of rotation driving and a disturbance with light and reflection. Therefore, the localization error of the laser navigation by the above-mentioned weakness has a great effect on the performance of the AGV. There are many different methods to correct the localization error, such as a method using a fuzzy inference system, a method with probabilistic method and so on. Bayes filter based estimation algorithms (Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter and Particle Filter) are mostly used to correct the localization error of the AGV. This paper analyses performance of estimation algorithms with probabilistic method at localization of the AGV. Algorithms for comparison are Extended Kalman Filter, Unscented Kalman Filter and Particle Filter. Kalman Filter is excluded to the comparison, because Kalman Filter is applied only to a linear system. For the performance comparison, a fork-type AGV is used to the experiments. Variables of algorithms is set experiments based heuristic values, and then variables of same functions on algorithms is set same values.

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot’s position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

The performance of arrow from a manufacturing process depends on arrow`s trajectory(archer`s paradox) and intensity of an impact points. Especially, when conducting a shooting experiment over and over in the same experiment condition, the intensity of impact point is an objective standard to judge the performance of the arrow. However, the analysis method for the impact point is not enough, a previous research of the arrow`s performance has been focused on a skill to optimize a manufacturing variables(feathers of an arrow, barb of an arrow, arrow`s shaft, weight, external diameter, spine). In this paper, We propose measurement system of arrow`s impact point with Mamdani fuzzy inference system and similarity of polygon for automation of impact point`s measurement. Measuring the impact point data of the arrow moving with a high speed(approximately 275km/h) by using line laser and photo diode array, then the measured data are mapped to arrow`s impact point with fuzzy inference and similarity of polygon.

Recently, pirates are infesting on the sea and they have been hijacking the several vessels for example Samho Dream and Samho Jewelry of Korea. One of the items to reduce the risk is to adopt the invader detection system. If the pirates break in to the ship, the detection system can monitor the pirates and then call the security alarm. The crew can gain time to hide to the safe room and the report can be automatically sent to the control room to cope with the situation. For the invader detection, an unmanned observation system was proposed using the image detection algorithm that extracts the invader image from the recording image. To detect the motion area, the difference value was calculated between the current image and the prior image of the invader, and the ‘AND’ operator was used in calculated image and edge line. The image noise was reduced based on the morphology operation and then the image was transformed into morphological information. Finally, a neural network model was applied to recognize the invader. In the experimental results, it was confirmed that the proposed approach can improve the performance of the recognition in the invader monitoring system.

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

In this paper, a novel planar type magnetic levitation system without other assistant devices is proposed and it can move with 6 degree of freedom (X, Y, Z, θX, θY, θZ) in wafer size as well as in nano scale positioning.The mover is composed with 2-D Halbach permanent magnet array and the stator is composed with 10 x 10 coil arrays.It was composed in laboratory and tested with short stroke (4 [㎜]) and long stroke (160 [mm])movements. The errors of short movement test is [X, Y, Z, θX, θY, θZ]≤ [±200㎚, ±200㎚, ±250㎚, ±3urad, ±2urad, ±1urad]The errors of long stroke movement test is [X, Y, Z, θX, θY, θZ]≤ [±200㎚, ±200㎚, ±250㎚, ±1.5urad, ±2urad, ±0.5urad].

This paper presents a development of an analog type magnetic positioning system and its positioning accuracy improvement using fuzzy inference system. As the magnetic positioning system used on a magnet-gyro guidance system for AGV(automatic guided vehicle), it measures a position of magnet embedded in floor of the work place. The existing product of the magnetic positioning system is very expensive in Korea because it is being sold in a foreign country exclusively. Moreover, the positioning accuracy of the product is low because it uses digital type unipolar hall sensors. Hence, we developed the magnetic positioning system by ourselves and improved the positioning accuracy of the developed magnetic positioning system using fuzzy inference system. For experiment, we used the analog type magnetic positioning system which we have developed, and compared the performance of the proposed method with the performance of the existing positioning method for the magnetic positioning system. In experimental results, we verified that the proposed method improved the positioning accuracy of the magnetic positioning system.

The point of impact, the shot group, and the flight traces depend on the combination of unique features which decide moving traces of the arrow (paradox of the archer, length of the arrow shaft, weight, angle of the feather, and spline of the arrow shaft). The more dense the impact points in the shot group and the earlier elimination of paradox of the archer, the higher assessment is given for the product. However, there is no way to objectively assess the efficiency and quality of the arrow, and there is no numeric data to be used as the basis for comparison with other products. Although capturing the images of flying arrow using a high-speed motion picture camera is possible, we are limited to observation from specific view angle only. Hence, the criteria for efficiency and quality assessment are mostly based on subjective opinions of experts or hunters, or review on consumers’ remarks. In this paper, we propose a hardware composition that are based on three detection frames consisting of line lasers and photo diode arrays without the high-speed motion picture camera. Predicated on measured coordinates data, a nobel method for the archer’s paradox measurement, a key parameter that determine the arrow’s trajectory, and corresponding numerical analysis model is proposed.

The Anaerobic/Anoxic/Oxic (A2/O) process is a traditionally well-established biological wastewater treatment process (WWTP). In order to control a biological process in a laboratory environment, engineers typically adopt a methodology that relies mostly on their prior knowledge of transient and steady-state behaviors of micro-organisms. Based on this prior knowledge, our A2/O process is designed to keep proper reaction time in check as well as the state defining conditions of micro-organisms. However, converse to our expectation, unforeseen experimental changes in our biological samples could cause the entire experimental process to deviate from its original course of progress. Practically, to mitigate these unexpected changes, modular neural networks (MNNs) prediction model for dissolved oxygen (DO) concentration is proposed. The MNNs consists of numerous neural network models. Each neural network model is only concerned with a single cluster. Therefore, when suitable neural network models can be selected for each condition, stable and advanced treatment performance is expected. DO concentration is an observable and controllable factor in a reactor. On the whole it affects the biological reactions and water quality. DO set-point is decided by prediction model and its control remains as an essential feature of WWTP. The predicted DO concentration is used to control the air blower, thus enabling scheduling for a stable NH4-N concentration in the effluent wastewater. The proposed model has time-series static neural networks with multiple inputs and one output. The inputs — the air blower speed and DO concentration — are time-dependent variables. The output is the DO concentration in the immediate following time step. Our prediction results are compared with those of other prediction methodologies; proposed prediction model shows that it can achieve better accuracy for the DO concentration estimate than by other models. Therefore, the proposed system can be applied to a time-delayed compensation system to meet the target DO set-point tracking.