Search results for: adaptive Neuro-Fuzzy Inference System (ANFIS)

Authors: Carolina B. Baguio

Abstract:

One of the purposes of the robust method of estimation is to reduce the influence of outliers in the data, on the estimates. The outliers arise from gross errors or contamination from distributions with long tails. The trimmed mean is a robust estimate. This means that it is not sensitive to violation of distributional assumptions of the data. It is called an adaptive estimate when the trimming proportion is determined from the data rather than being fixed a “priori-. The main objective of this study is to find out the robustness properties of the adaptive trimmed means in terms of efficiency, high breakdown point and influence function. Specifically, it seeks to find out the magnitude of the trimming proportion of the adaptive trimmed mean which will yield efficient and robust estimates of the parameter for data which follow a modified Weibull distribution with parameter λ = 1/2 , where the trimming proportion is determined by a ratio of two trimmed means defined as the tail length. Secondly, the asymptotic properties of the tail length and the trimmed means are also investigated. Finally, a comparison is made on the efficiency of the adaptive trimmed means in terms of the standard deviation for the trimming proportions and when these were fixed a “priori". The asymptotic tail lengths defined as the ratio of two trimmed means and the asymptotic variances were computed by using the formulas derived. While the values of the standard deviations for the derived tail lengths for data of size 40 simulated from a Weibull distribution were computed for 100 iterations using a computer program written in Pascal language. The findings of the study revealed that the tail lengths of the Weibull distribution increase in magnitudes as the trimming proportions increase, the measure of the tail length and the adaptive trimmed mean are asymptotically independent as the number of observations n becomes very large or approaching infinity, the tail length is asymptotically distributed as the ratio of two independent normal random variables, and the asymptotic variances decrease as the trimming proportions increase. The simulation study revealed empirically that the standard error of the adaptive trimmed mean using the ratio of tail lengths is relatively smaller for different values of trimming proportions than its counterpart when the trimming proportions were fixed a 'priori'.

Keywords: Adaptive robust estimate, asymptotic efficiency, breakdown point, influence function, L-estimates, location parameter, tail length, Weibull distribution.

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Dylan Menzies, Ismail Shahin

Abstract:

This paper describes a new efficient blind source separation method; in this method we uses a non-uniform filter bank and a new structure with different sub-bands. This method provides a reduced permutation and increased convergence speed comparing to the full-band algorithm. Recently, some structures have been suggested to deal with two problems: reducing permutation and increasing the speed of convergence of the adaptive algorithm for correlated input signals. The permutation problem is avoided with the use of adaptive filters of orders less than the full-band adaptive filter, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full-band, and can promote better rates of convergence.

Keywords: Blind source separation (BSS), estimates, full-band, mixtures, Sub-band.

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Authors: Elizabeth B. Varghese, M. Wilscy

Abstract:

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: Kuen-Long Leu, Yung-Yuan Chen, Chin-Long Wey, Jwu-E Chen, Chung-Hsien Hsu

Abstract:

The increasing importance of FlexRay systems in automotive domain inspires unceasingly relative researches. One primary issue among researches is to verify the reliability of FlexRay systems either from protocol aspect or from system design aspect. However, research rarely discusses the effect of network topology on the system reliability. In this paper, we will illustrate how to model the reliability of FlexRay systems with various network topologies by a well-known probabilistic reasoning technology, Bayesian Network. In this illustration, we especially investigate the effectiveness of error containment built in star topology and fault-tolerant midpoint synchronization algorithm adopted in FlexRay communication protocol. Through a FlexRay steer-by-wire case study, the influence of different topologies on the failure probability of the FlexRay steerby- wire system is demonstrated. The notable value of this research is to show that the Bayesian Network inference is a powerful and feasible method for the reliability assessment of FlexRay systems.

Keywords: Bayesian Network, FlexRay, fault tolerance, network topology, reliability.

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Authors: Samee Ullah Khan, C.Ardil

Abstract:

With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.

Keywords: Meta-heuristics, distributed systems, adaptive methods, resource allocation.

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Authors: A. Denker, U. Akıncıoğlu

Abstract:

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which combines the advantages of both direct inverse control and predictive control.

Keywords: Neural networks, robotics, direct inverse control, predictive control.

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Authors: Vassilis S. Kodogiannis, John N. Lygouras

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In this research study, an intelligent detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the M2A Swallowable Imaging Capsule - a patented, video color-imaging disposable capsule. Schemes have been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from each color component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The achieved high detection accuracy of the proposed system has provided thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy.

Keywords: Medical imaging, Computer aided diagnosis, Endoscopy, Neuro-fuzzy networks, Fuzzy integral.

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Authors: N. Ben Si Ali, N. Benalia, N. Zarzouri

Abstract:

Speed sensorless systems are intensively studied during recent years; this is mainly due to their economical benefit and fragility of mechanical sensors and also the difficulty of installing this type of sensor in many applications. These systems suffer from instability problems and sensitivity to parameter mismatch at low speed operation. In this paper an analysis of adaptive observer stability with stator resistance estimation is given.

Keywords: Motor drive, sensorless control, adaptive observer, stator resistance estimation.

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Authors: Mahmood Reza Shakarami, Reza Sedaghati

Abstract:

Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.

Keywords: Adaptive Modified Firefly Optimization (AMFO), Pareto solutions, feeder reconfiguration, wind turbines, bus voltage.

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Authors: Chunming Xu

Abstract:

In this paper, the finite-time symplectic synchronization between two different chaotic systems is investigated. Based on the finite-time stability theory, a simple adaptive feedback scheme is proposed to realize finite-time symplectic synchronization for the Lorenz and L¨u systems. Numerical examples are provided to show the effectiveness of the proposed method.

Keywords: Chaotic systems, symplectic synchronization, finite-time synchronization, adaptive controller.

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Authors: M. H. Imam, Imran A. Tasadduq, Abdul-Rahim Ahmad, Fahd M. Aldosari

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Learning outcomes of a course (CLOs) and the abilities at the time of graduation referred to as Student Outcomes (SOs) are required to be assessed for ABET accreditation. A question in an assessment must target a CLO as well as an SO and must represent a required level of competence. This paper presents the idea of an Expert System (ES) to select a proper question to satisfy ABET accreditation requirements. For ES implementation, seven attributes of a question are considered including the learning outcomes and Bloom’s Taxonomy level. A database contains all the data about a course including course content topics, course learning outcomes and the CLO-SO relationship matrix. The knowledge base of the presented ES contains a pool of questions each with tags of the specified attributes. Questions and the attributes represent expert opinions. With implicit rule base the inference engine finds the best possible question satisfying the required attributes. It is shown that the novel idea of such an ES can be implemented and applied to a course with success. An application example is presented to demonstrate the working of the proposed ES.

Keywords: Expert system, student outcomes, course learning outcomes, question attributes.

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Authors: T. Sattarpour, D. Nazarpour

Abstract:

This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: Active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF).

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Authors: Yi-Haur Shiau, Sun-In Lin, Shi-Wei Lo, Hsiu-Mei Chou, Yi-Hsuan Chen

Abstract:

This paper proposes a novel multi-format stream grid architecture for real-time image monitoring system. The system, based on a three-tier architecture, includes stream receiving unit, stream processor unit, and presentation unit. It is a distributed computing and a loose coupling architecture. The benefit is the amount of required servers can be adjusted depending on the loading of the image monitoring system. The stream receive unit supports multi capture source devices and multi-format stream compress encoder. Stream processor unit includes three modules; they are stream clipping module, image processing module and image management module. Presentation unit can display image data on several different platforms. We verified the proposed grid architecture with an actual test of image monitoring. We used a fast image matching method with the adjustable parameters for different monitoring situations. Background subtraction method is also implemented in the system. Experimental results showed that the proposed architecture is robust, adaptive, and powerful in the image monitoring system.

Keywords: Motion detection, grid architecture, image monitoring system, and background subtraction.

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Authors: Frank Emmert-Streib, Matthias Dehmer

Abstract:

Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Keywords: Dynamic Bayesian networks, microarray data, structure learning, Markov chain Monte Carlo.

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Authors: Mohammad Ali Tabarzad, Caro Lucas, Ali Hamzeh

Abstract:

Adaptive Genetic Algorithms extend the Standard Gas to use dynamic procedures to apply evolutionary operators such as crossover, mutation and selection. In this paper, we try to propose a new adaptive genetic algorithm, which is based on the statistical information of the population as a guideline to tune its crossover, selection and mutation operators. This algorithms is called Statistical Genetic Algorithm and is compared with traditional GA in some benchmark problems.

Keywords: Genetic Algorithms, Statistical Information ofthe Population, PAUX, SSO.

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Authors: Franco Frattolillo

Abstract:

The advances in multimedia and networking technologies have created opportunities for Internet pirates, who can easily copy multimedia contents and illegally distribute them on the Internet, thus violating the legal rights of content owners. This paper describes how a simple and well-known watermarking procedure based on a spread spectrum method and a watermark recovery by correlation can be improved to effectively and adaptively protect MPEG-2 videos distributed on the Internet. In fact, the procedure, in its simplest form, is vulnerable to a variety of attacks. However, its security and robustness have been increased, and its behavior has been made adaptive with respect to the video terminals used to open the videos and the network transactions carried out to deliver them to buyers. In fact, such an adaptive behavior enables the proposed procedure to efficiently embed watermarks, and this characteristic makes the procedure well suited to be exploited in web contexts, where watermarks usually generated from fingerprinting codes have to be inserted into the distributed videos “on the fly", i.e. during the purchase web transactions.

Keywords: Copyright protection, digital watermarking, intellectualproperty protection.

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Authors: Xinyue Zhao, Yutaka Satoh, Hidenori Takauji, Shun'ichi Kaneko

Abstract:

A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera. The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental results show the good performance of the proposed method.

Keywords: Hybrid feature, adaptive Particle Filter, robust Object Tracking, Grayscale Arranging Pairs (GAP) feature.

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Authors: Anil Ranjitbhai Patel, Clement John Shaji, Peter Liggesmeyer

Abstract:

Safety and security of Autonomous Vehicles (AVs) is a growing concern, first, due to the increased number of safety-critical functions taken over by automotive embedded systems; second, due to the increased exposure of the software-intensive systems to potential attackers; third, due to dynamic interaction in an uncertain and unknown environment at runtime which results in changed functional and non-functional properties of the system. Frequently occurring environmental uncertainties, random component failures, and compromise security of the AVs might result in hazardous events, sometimes even in an accident, if left undetected. Beyond these technical issues, we argue that the safety and security of AVs against accidental and deliberate faults are poorly understood and rarely implemented. One possible way to overcome this is through a well-known diversity approach. As an effective approach to increase safety and security, diversity has been widely used in the aviation, railway, and aerospace industries. Thus, paper proposes fault-tolerance by diversity model taking into consideration the mitigation of accidental and deliberate faults by application of structure and variant redundancy. The model can be used to design the AVs with various types of diversity in hardware and software-based multi-version system. The paper evaluates the presented approach by employing an example from adaptive cruise control, followed by discussing the case study with initial findings.

Keywords: Autonomous vehicles, diversity, fault-tolerance, adaptive cruise control, safety, security.

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Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

Abstract:

Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.

Keywords: Adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication.

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Authors: Je Young Lee, Hee Sung Kim, Kwang Ho Choi, Joonhoo Lim, Sebum Chun, Hyung Keun Lee

Abstract:

This study is purposed to develop an efficient fault detection method for Global Navigation Satellite Systems (GNSS) applications based on adaptive noise covariance estimation. Due to the dependence on radio frequency signals, GNSS measurements are dominated by systematic errors in receiver’s operating environment. In the proposed method, the pseudorange and carrier-phase measurement noise covariances are obtained at time propagations and measurement updates in process of Carrier-Smoothed Code (CSC) filtering, respectively. The test statistics for fault detection are generated by the estimated measurement noise covariances. To evaluate the fault detection capability, intentional faults were added to the filed-collected measurements. The experiment result shows that the proposed method is efficient in detecting unhealthy measurements and improves GNSS positioning accuracy against fault occurrences.

Keywords: Adaptive estimation, fault detection, GNSS, residual.

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Authors: Norihiro Nishio, Yuki Deguchi, Takahiro Sugiyama, Yoichi Takebayashi

Abstract:

We developed a multi-camera control system that a (one) cameraman can operate several cameras at a compact studio. we analyzed a workflow of a cameraman of some program shootings with two cameras and clarified their heavy tasks. The system based on a dynamic workflow which adapts a program progressing and recommends of cameraman. we perform the automation of multicamera controls by modeling of studio environment and perform automatic camera adjustment for suitable angle of view with face detection. Our experiment at a real program shooting showed that one cameraman can carry out the task of shooting sufficiently.

Keywords: Camera work, compact studio, dynamic workflow, shooting support.

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Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

Abstract:

Keywords: Structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames.}

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Authors: Tao Dai, Andy Adler, Behnam Shahrrava

Abstract:

This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.

Keywords: Adaptive signal processing, affine projection algorithms, variable step-size adaptive algorithms, regularization.

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Authors: Anamika Jain, A. S. Thoke, R. N. Patel

Abstract:

This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.

Keywords: Double circuit transmission line, Fault detection and classification, High impedance fault and Artificial Neural Network.

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Authors: Mohammad Shams Esfand Abadi, Nader Hadizadeh Kashani, Vahid Mehrdad

Abstract:

In this paper we present a general formalism for the establishment of the family of selective regressor affine projection algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-RAPA), the SR partial rank algorithm (SR-PRA), the SR binormalized data reusing least mean squares (SR-BNDR-LMS), and the SR normalized LMS with orthogonal correction factors (SR-NLMS-OCF) algorithms are established by this general formalism. We demonstrate the performance of the presented algorithms through simulations in acoustic echo cancellation scenario.

Keywords: Adaptive filter, affine projection, selective regressor.

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Authors: Michela Lecca, Stefano Messelodi

Abstract:

A new automatic system for the recognition and re¬construction of resealed and/or rotated partially occluded objects is presented. The objects to be recognized are described by 2D views and each view is occluded by several half-planes. The whole object views and their visible parts (linear cuts) are then stored in a database. To establish if a region R of an input image represents an object possibly occluded, the system generates a set of linear cuts of R and compare them with the elements in the database. Each linear cut of R is associated to the most similar database linear cut. R is recognized as an instance of the object 0 if the majority of the linear cuts of R are associated to a linear cut of views of 0. In the case of recognition, the system reconstructs the occluded part of R and determines the scale factor and the orientation in the image plane of the recognized object view. The system has been tested on two different datasets of objects, showing good performance both in terms of recognition and reconstruction accuracy.

Keywords: Occluded Object Recognition, Shape Reconstruction, Automatic Self-Adaptive Systems, Linear Cut.

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Authors: Imron Rosyadi, Ridha A. Djemal, Saleh A. Alshebeili

Abstract:

Design and implementation of a novel B-ACOSD CFAR algorithm is presented in this paper. It is proposed for detecting radar target in log-normal distribution environment. The BACOSD detector is capable to detect automatically the number interference target in the reference cells and detect the real target by an adaptive threshold. The detector is implemented as a System on Chip on FPGA Altera Stratix II using parallelism and pipelining technique. For a reference window of length 16 cells, the experimental results showed that the processor works properly with a processing speed up to 115.13MHz and processing time0.29 ┬Ás, thus meets real-time requirement for a typical radar system.

Keywords: CFAR, FPGA, radar.

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Authors: Mousmita Sarma, Krishna Dutta, Kandarpa Kumar Sarma

Abstract:

Speech corpus is one of the major components in a Speech Processing System where one of the primary requirements is to recognize an input sample. The quality and details captured in speech corpus directly affects the precision of recognition. The current work proposes a platform for speech corpus generation using an adaptive LMS filter and LPC cepstrum, as a part of an ANN based Speech Recognition System which is exclusively designed to recognize isolated numerals of Assamese language- a major language in the North Eastern part of India. The work focuses on designing an optimal feature extraction block and a few ANN based cooperative architectures so that the performance of the Speech Recognition System can be improved.

Keywords: Filter, Feature, LMS, LPC, Cepstrum, ANN.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

Abstract:

Mostly the systems are dealing with time varying signals. The Power efficiency can be achieved by adapting the system activity according to the input signal variations. In this context an adaptive rate filtering technique, based on the level crossing sampling is devised. It adapts the sampling frequency and the filter order by following the input signal local variations. Thus, it correlates the processing activity with the signal variations. Interpolation is required in the proposed technique. A drastic reduction in the interpolation error is achieved by employing the symmetry during the interpolation process. Processing error of the proposed technique is calculated. The computational complexity of the proposed filtering technique is deduced and compared to the classical one. Results promise a significant gain of the computational efficiency and hence of the power consumption.

Keywords: Level Crossing Sampling, Activity Selection, Rate Filtering, Computational Complexity, Interpolation Error.

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Authors: Dongho Kim, Sung Choi, Kyowoong Pee, Youngsik Cho, Seungwoo Jeong, Soo-Ho Kim

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This study is about the orbital TIG welding robot system which travels on the guide rail installed on the pipe, and welds and tracks the pipe seam using the LVS (Laser Vision Sensor) joint profile data. The orbital welding robot system consists of the robot, welder, controller, and LVS. Moreover we can define the relationship between welding travel speed and wire feed speed, and we can make the linear equation using the maximum and minimum amount of weld metal. Using the linear equation we can determine the welding travel speed and the wire feed speed accurately corresponding to the area of weld captured by LVS. We applied this orbital TIG welding robot system to the stainless steel or duplex pipe on DSME (Daewoo Shipbuilding and Marine Engineering Co. Ltd.,) shipyard and the result of radiographic test is almost perfect. (Defect rate: 0.033%).

Keywords: Adaptive welding, automatic welding, Pipe welding, Orbital welding, Laser vision sensor, LVS, welding D/B.

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