Search

The Cygnus Dual Beam Radiographic Facility consists of two identical radiographic sources each with a dose rating of ∼4-rad at 1 m, and a ∼1-mm diameter spot size. The development of the rod pinch diode (RPD) was responsible for the ability to meet these criteria. The RPD in each machine uses a 0.75-mm tungsten diameter tapered anode rod extended through a 9-mm diameter cathode aperture. When properly configured, the electron beam born off the aperture edge can self-insulate and pinch onto the tip of the rod creating an intense, small x-ray source. These sources are utilized in concert with an imaging system on complex experiments, which are single-shot, high-value events. In such an application there is an emphasis on machine performance (reliability and reproducibility). Vacuum quality is a significant determinator for source performance. In this paper we discuss the Cygnus Dual Beam Radiographic Facility, the associated vacuum systems, vacuum monitor calibration and a multi-purpose vacuum chamber. Vacuum system performance, using various enhancements, will be reported. Using the vacuum system enhancements, as well as other machine tweaks, recent source performance has improved. This new level of performance will be presented via dosimetry and electrical measurements.

Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS.

Design of analog and digital electronic circuits is an integral part of the electrical engineering curriculum. Software tools, which enable design, simulation, measuring and circuit analysis as well as PCB (Printed Circuit Board) design, are called EDA (Electronic Design Automation). The paper describes the introduction of EDA software in the first semester of the professional study of electrical engineering at the Zagreb University of Applied Sciences within course Personal Computers in Electrical Engineering. Examples of laboratory exercises related to electrical circuit diagrams, use of component libraries, work in standard laboratory environment (power supplies, function generators, voltmeters, ammeters, oscilloscopes, etc.), circuit analysis in a DC and AC mode as well as in time and frequency domain are described. Focus is on the optimal implementation of EDA tool NI Multisim and its integration with the existing learning management system Moodle as a platform for exercise learning and knowledge assessment.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

Motor current signature analysis is the reference method for the diagnosis of induction machines. However, it features some drawbacks, as it is bound to steady-state conditions, and fault signatures may be created by different overlapping phenomena. This paper presents a full-time domain-based method for the quantitative evaluation of electrical faults in induction machines. It relies on stator current measurements; thus, it is compliant with switching power converter drives, provided that a proper current measurement technique is available. Moreover, it allows an accurate quantification of faults without requiring spectrum analysis; hence, it achieves high resolution even with a low time acquisition period. A fault index is computed as the energy of the signal in a suitable ldquofault bandwidth,rdquo thus reducing the effects of frequency variations. The aforementioned advantages are perfectly suited to time varying signals and to embedded drive diagnostic systems.

In order to sell items of a genre that each user is not interested in, their latent interests of the genre have to be induced. Therefore, we focus on "Influencer," who is the person that has a large impact on a user's behaviors, and research a recommender system which advertises some items by utilizing influencer's remarks via social media. This paper investigates what kinds of remarks and remarkers would induce users'interests. As the result, we have revealed many findings. One is that "One of the factors that would induce the users' positive interests on the movie, is the positive reputation for the movie that is included in tweets." The other is that "If the user does not like the remarker, the user's negative interests for the items in the remarks of the person would be induced, even though the person has famousness.", INSPEC Accession Number: 18674306

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more effectively than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the state-of-the-art on UCF101 and HMDB datasets., Comment: Accepted in WACV 2017

In this paper, three main criteria for the adaptive beamformer design are discussed: maximal signal-to-interference-plus-noise ratio (MSINR), minimal mean-squared error (MMSE), and minimal least-square error (MLSE). Although in the case of exactly known power and steering vector of the signal-of-interest (SOI), there are beamformers that can simultaneously meet the MMSE and MSINR criteria, this is no longer true when the exact knowledge of the steering vector is unavailable. To account for steering vector errors, a meaningful approach is to model the actual steering vector as random. In this paper, it is shown that in the latter case, the MMSE and MSINR criteria can not be simultaneously attained. We study the achievable region in the MSE-SINR plane and propose a new adaptive beamformer that can attain a frontier of operating points on the boundary of this region and, therefore, provide an optimal performance tradeoff among the MSINR and MMSE criteria. It is also shown that in the random steering vector case, the MLSE and MSINR criteria are simultaneously achievable and a new adaptive beamformer is proposed that satisfies both these criteria.

In this paper we propose to utilize a new concept of discrete directional Gabor frames for automatic image registration. The directional Gabor representations have been shown to provide more accurate feature extraction than directional wavelet transforms for images where texture is the dominant feature. Initial experimental results are presented here which indicate that discrete directional Gabor frames exhibit strong correlations, which indicates that they are likely to improve the existing image registration toolbox.

Several optimal design schemes of a single-sided linear induction motor (SLIM) adopted in linear metro are presented in this paper. Firstly, the equivalent circuit of SLIM fully considering the end effects, half-filled slots, back-iron saturation and skin effect is proposed, based on one-dimensional air-gap magnetic equations. In the circuit, several coefficients including longitudinal end effect coefficients K r (s) and K x (s), transversal end edge effect coefficients C r (s) and Cx(s), and skin effect coefficient K f are achieved by using the dummy electric potential method and complex power equivalence between primary and secondary sides. Furthermore, several optimal design restraint equations of SLIM are provided in order to improve the operational efficiency and reduce the primary weight. These nonlinear equations are solved by using genetic algorithm and mixture penalty function method. The optimal schemes are compared with the original design of one company, where analysis on parameters is made in detail. These results show that the optimal schemes are reasonable for improving the performance of SLIM.

Accurately modeling power distribution grids is crucial for designing effective monitoring and decision making algorithms. This paper addresses the partial observability issue of data-driven distribution modeling in order to improve the accuracy of line parameter estimation. Inspired by the sparse changes in residential loads, we advocate to regularize the group sparsity of the unobservable injections in a bi-linear estimation problem. The alternating minimization scheme of guaranteed convergence is proposed to take advantage of convex subproblems with efficient solutions. Numerical results using real-world load data on the single-phase equivalent of the IEEE 123-bus test case have demonstrated the accuracy improvements of the proposed solution over existing work for both parameter estimation and voltage modeling.

In today's globalized business world, outsourcing, joint ventures, and cross-border collaborations have led to work environments that are geographically distributed across organizational and national boundaries. There are critical research needs to develop highly secured collaborative work environments and security solutions for deployment, configuration, monitoring, and device control of interoperating services. This paper presents a well-shaped security framework for distributed system control with a focus on device-level system control, monitoring and services re-configuration in open and dynamic environments. The characteristics of portability, reconfigurability, interoperability, and interchangeability of these new environments are considered as key factors to produce new security risks and challenges. By adopting Public Key cryptography, software agent and XML binding technologies, the major security problems of authenticity, integrity, confidentiality, and safe execution are addressed in this framework. The core modules for secure task delivery and execution are presented in detail., The 1st IEEE Conference on Industrial Informatics, August 2003, Banff, Alberta, Canada

In broadcast fading channel, channel variations can be exploited through what is referred to as multi-user diversity and opportunistic scheduling for improving system performance. To achieve the gains promised by this kind of diversity, the transmitter has to accurately track the channel variations of the various receivers, which consumes resources (time, energy, bandwidth), and thus reduces the resources remaining for effective data transmissions. The transmitter may decide not to acquire or probe the channel conditions of certain receivers, either because these receivers are presumably experiencing severe fading, or because the transmitter wishes to spare resources for data transmissions. It may also decide to transmit to a receiver without probing its channel; in such cases, the transmitter guesses the channel state, which often results in a reduction of the transmission rate compared to when the transmitter knows the channel state. Ultimately, the transmitter has to decide to which receiver it should transmit. In this paper, we identifying the joint probing and transmission strategies realizing the optimal trade-off between the channel state acquisition and the effective data transmission. The objective is to maximize the system throughput. Finally, we propose several extensions of the proposed strategy, including a scheme to maximize the system utility and a scheme to ensure the system stability., 10.1109/78.845940, © IEEE

This paper presents ASC03 algorithm, aimed at finding mappings between entities of two ontologies represented in OWL DL/Lite. ASC03 algorithm searches the maximal common subgraph of two graphs representing two ontologies, and relies on an algorithm of search of the maximal clique of their association graph.

Many applications that can take advantage of accelerators are amenable to approximate execution. Past work has shown that neural acceleration is a viable way to accelerate approximate code. In light of the growing availability of on-chip field-programmable gate arrays (FPGAs), this paper explores neural acceleration on off-the-shelf programmable SoCs. We describe the design and implementation of SNNAP, a flexible FPGA-based neural accelerator for approximate programs. SNNAP is designed to work with a compiler workflow that configures the neural network's topology and weights instead of the programmable logic of the FPGA itself. This approach enables effective use of neural acceleration in commercially available devices and accelerates different applications without costly FPGA reconfigurations. No hardware expertise is required to accelerate software with SNNAP, so the effort required can be substantially lower than custom hardware design for an FPGA fabric and possibly even lower than current “C-to-gates” high-level synthesis (HLS) tools. Our measurements on a Xilinx Zynq FPGA show that SNNAP yields a geometric mean of 3.8× speedup (as high as 38.1×) and 2.8× energy savings (as high as 28 x) with less than 10% quality loss across all applications but one. We also compare SNNAP with designs generated by commercial HLS tools and show that SNNAP has similar performance overall, with better resource-normalized throughput on 4 out of 7 benchmarks.

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.

New tendencies envisage 3D Multi-Processor System-On-Chip (MPSoC) design as a promising solution to keep increasing the performance of the next-generation high-performance computing (HPC) systems. However, as the power density of HPC systems increases with the arrival of 3D MPSoCs, supplying electrical power to the computing equipment and constantly removing the generated heat is rapidly becoming the dominant cost in any HPC facility. Thus, both power and thermal/cooling implications play a major role in the design of new HPC systems, given the energy constraints in our society. Therefore, EPFL, IBM and ETHZ have been working within the CMOSAIC Nano-Tera.ch program project in the last three years on the development of a holistic thermally-aware design. This paper presents the exploration in CMOSAIC of novel cooling technologies, as well as suitable thermal modeling and system-level design methods, which are all necessary to develop 3D MPSoCs with inter-tier liquid cooling systems. As a result, we develop energy-efficient run-time thermal control strategies to achieve energy-efficient cooling mechanisms to compress almost 1 Tera nano-sized functional units into one cubic centimeter with a 10 to 100 fold higher connectivity than otherwise possible. The proposed thermally-aware design paradigm includes exploring the synergies of hardware-, software- and mechanical-based thermal control techniques as a fundamental step to design 3D MPSoCs for HPC systems. More precisely, we target the use of inter-tier coolants ranging from liquid water and two-phase refrigerants to novel engineered environmentally friendly nano-fluids, as well as using specifically designed micro-channel arrangements, in combination with the use of dynamic thermal management at system-level to tune the flow rate of the coolant in each micro-channel to achieve thermally-balanced 3D-ICs. Our management strategy prevents the system from surpassing the given threshold temperature while achieving up to 67% reduction in cooling energy and up to 30% reduction in system-level energy in comparison to setting the flow rate at the maximum value to handle the worst-case temperature.

This paper describes a line-of-sight (LOS) channel simulator that can be used for the laboratory evaluation of microwave radios. The simulator has a unique design, utilizes state-of-the-art microwave circuitry, and is completely under computer control. The hardware and software design concepts incorporated in the simulator are described. Results from tests conducted on a military microwave radio are presented. The results show the sensitivity of an unprotected radio to small changes in amplitude distortion of the received signal. Static m-curve signatures are also presented. The need to simulate the dynamics of frequency selective fading is described. Small changes to the simulator which will provide such dynamic operation are planned.

Pulse-width modulated (PWM) inverters are known to generate common mode voltages which cause motor bearing currents in the induction motor drives. They also result in leakage currents which act as sources of conducted electromagnetic interference in the drive system. The common mode voltage generated by a conventional three-level inverter can be eliminated by switching only the voltage space vectors which do not produce the common mode voltage. This paper presents a PWM switching strategy to eliminate common mode voltage using the open-end winding configuration for the induction motor. The switching strategy presented in this paper, does not generate any alternating common mode voltages in the drive system and hence the electrostatic coupling of the common mode voltage, which results in the bearing currents and the leakage currents, is avoided. The proposed scheme is devoid of neutral point voltage fluctuations and does not require neutral point clamping diodes, when compared to the common mode elimination scheme based on the conventional three-level inverter topology. Also, the present scheme uses a single dc-link with half the voltage compared to the conventional three-level inverter based scheme.

The positioning systems’ high accuracy and reliability are crucial enablers for various future applications, including autonomous shipping worldwide. It is especially challenging for the Arctic region due to the lower number of visible satellites, severe ionospheric disturbances, scintillation effects, and higher delays than in the non-Arctic and non-Antarctic regions. In regions up North, conventional satellite positioning systems are generally proposed to be utilized, together with other situational awareness systems, to achieve the necessary level of accuracy. This paper provides a detailed review of the current state-of-the-art, satellite-based positioning systems’ availability and performance and reports high-level positioning requirements for the oncoming applications. In particular, the comparative study between three Global Navigation Satellite System (GNSS) constellations is executed to determine whether they are suitable for autonomous vessel navigation in the Arctics’ complex environment as the two most significant drivers for a reevaluation of the related satellite constellations. This work analyzes the ongoing research executed in different (inter-) national projects focused on Galileo, Global Positioning System (GPS), and GLObal NAvigation Satellite System (GLONASS). Based on the literature review and the simulation campaign, we conclude that all the convectional constellations achieve an accuracy of fewer than three meters in the analyzed Arctic scenarios. It is postulated that other complementary positioning methods should be utilized to improve accuracy beyond this limit. Finally, the study emphasizes existing challenges in the Arctic region regarding the localization and telecommunication capabilities and provides future research directions. publishedVersion

Emerging as a widely accepted technique for malware analysis, YARA rules due to its flexible and customisable nature, allows malware analysts to develop rules according to the requirements of a specific security domain. YARA rules can be automatically generated using tools, however, they may require post-processing for their optimisation, and may not be effective for the specific security domain. This compels the requirement to enhance automatically generated YARA rules and increase their effectiveness for malware analysis without increasing computa- tional overheads. Reflecting on the above requirement, this paper initially evaluates automatically generated YARA rules using three YARA tools: yarGen, yaraGenerator and yabin. These tools are Python-based open-source tools used to generate YARA rules automatically utilising different underlying techniques. Subsequently, it proposes a method to enhance automatically generated YARA rules using a fuzzy hashing method. This proposed enhancement method can improve the effectiveness of YARA rules irrespective of the chosen YARA tool used to generate YARA rules, which is demonstrated through several experiments on samples of collected malware and goodware.

In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region candidates are generated through a modified K-means clustering algorithm, which references texture features, edge information and color information. The candidate labels are then verified in a global sense by the Markov Random Field model where collinearity weight is added as long as most texts are aligned. The proposed method achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database.

This paper presents a novel human joint motion recording method, The recorded data are sent to the receiver, which is placed in the close proximity or in the same room with the patient, via a wireless short-range communications system that guarantees 3 days of battery life. This method exploits commercially available bend sensors to convert mechanical human joint movements into electric signals which are then acquired, pre-processed, wireless transmitted and post-processed. We propose a novel way of sensor's application, underlying advantages and drawbacks which could be drastically reduced by electronic circuitry anyway. The network configuration and the specific air interface are chosen to satisfy system requirements in terms of data rates, battery autonomy, and mobility.

This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.

In daily life, people have to perform a large number of activities typically in a limited amount of time. Accordingly, they may benefit from help and guidance provided by support systems in order to accomplish these activities accurately and in the correct order. In order to satisfy such needs we develop a software framework which also incorporates a mobile application. Within the framework, workflows are used for modeling user activities due to their successful structuring and verification capabilities. However, stand-alone usage of workflows for personal activities is not enough without relating activities with the users' context. Thus, we augment user activities with environmental and web resources, sensors, mobile phone features, and provide advanced assistance compared to the general workflows. This augmentation also enables to enrich user activities by relating them with supplementary resources (text, image, audio, and video) provided by users or workflow designers. Furthermore, using the relationships between activities and context resources we automate the user's works by defining rules within the framework. Yet another important issue is to provide a constant communication that minimizes the network usage, since users are generally mobile. For this purpose, we set up a topic-based publish/subscribe messaging system infrastructure. Finally, in this paper we illustrate the execution of the smart environment scenario in order to demonstrate the applicability of the framework.

The recent popularity of sensor networks and cloud computing has brought new opportunities of sensor-cloud integration that facilitates users to immediately forecast the future status of what they are observing. The main challenge is how to establish a series of elastic data channels from sensors to mobile users through data-analyzing jobs running in the cloud. The variation of network protocols, the dynamism of cloud-computing resources, and even user mobility result in the following three design problems. Data-analyzing programs must: (1) deal with the major sensor and cloud communication protocols, (2) include hard-coded sensor manipulation and data retrieval operations, and (3) handle job/user migration in dynamic cloud environments, which needs the recovery of broken data connections. To address these problems, we are developing the Connector software tool kit that provides cloud jobs with uniform data channels, allows them to exchange standard input/output as well as graphics with remote users, and re-establishes their data channels upon a job or user migration. This paper presents Connector's system overview, implementation, and performance consideration.

In terms of Image-to-image translation, Generative Adversarial Networks (GANs) has achieved great success even when it is used in the unsupervised dataset. In this work, we aim to translate cartoon images to photo-realistic images using GAN. We apply several state-of-the-art models to perform this task; however, they fail to perform good quality translations. We observe that the shallow difference between these two domains causes this issue. Based on this idea, we propose a method based on CycleGAN model for image translation from cartoon domain to photo-realistic domain. To make our model efficient, we implemented Spectral Normalization which added stability in our model. We demonstrate our experimental results and show that our proposed model has achieved the lowest Frechet Inception Distance score and better results compared to another state-of-the-art technique, UNIT., Accepted as a short paper at ICTAI 2020

Data compression methods commonly focus on mean-square error (MSE) distortion. However, in TDOA/FDOA emitter location the distortion measure should focus on parameter accuracy. This paper discusses the proper choice of a distortion measure for this problem. We show that MSE is a poor choice for two reasons: (i) increased MSE may not even effect the parameter accuracy, and (ii) there are other factors that must be considered (e.g., the signal's RMS bandwidth/duration and the relative usefulness of TDOA vs. FDOA). We propose a new measure that captures the true impact of compression and illustrate its importance through a few simple examples.

This paper presents a lane change decision algorithm for predictive decision-making for an autonomous vehicle using a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (Bi-LSTM) cell. The proposed decision-making algorithm was trained and validated by driving data collected by vision, laser scanners, and chassis sensors of autonomous vehicles. The input features for the Bi-LSTM based RNN consist of the clearance and relative velocity with the surrounding target vehicles, lane measurements, and the velocity of the autonomous vehicle. The output features are configured to generate the probability of three maneuvers, left lane change, right lane change, and lane-keeping. The Bi-LSTM based RNN is configured to decide in advance two seconds before lane changes by using two seconds of observation. The collected 20,108 datasets were accumulated in global coordinates. After processing and resampling the collected datasets, 1,120, 320, and 160 datasets were generated to train, validate, and test the Bi-LSTM based RNN. The proposed algorithm was evaluated by a case study and a driving data-based prediction accuracy analysis. The results of the predictive lane change decision by the proposed algorithm have been shown to be more accurate and similar to a driver than previous approaches.

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model