Showing papers in "IEEE Transactions on Industrial Informatics in 2012"

A Survey on the Electrification of Transportation in a Smart Grid Environment

Abstract: Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.

A High Efficiency 5 kW Inductive Charger for EVs Using Dual Side Control

Abstract: This paper presents the design of a 5 kW inductive charging system for electric vehicles (EVs). Over 90% efficiency is maintained from grid to battery across a wide range of coupling conditions at full load. Experimental measurements show that the magnetic field strength meets the stringent International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines for human safety. In addition, a new dual side control scheme is proposed to optimize system level efficiency. Experimental validation showed that a 7% efficiency increase and 25% loss reduction under light load conditions is achievable. The authors believe this paper is the first to show such high measured efficiencies for a level 2 inductive charging system. Performance of this order would indicate that inductive charging systems are reasonably energy efficient when compared to the efficiency of plug-in charging systems.

Selection of Proper Neural Network Sizes and Architectures—A Comparative Study

Abstract: One of the major difficulties facing researchers using neural networks is the selection of the proper size and topology of the networks. The problem is even more complex because often when the neural network is trained to very small errors, it may not respond properly for patterns not used in the training process. A partial solution proposed to this problem is to use the least possible number of neurons along with a large number of training patterns. The discussion consists of three main parts: first, different learning algorithms, including the Error Back Propagation (EBP) algorithm, the Levenberg Marquardt (LM) algorithm, and the recently developed Neuron-by-Neuron (NBN) algorithm, are discussed and compared based on several benchmark problems; second, the efficiency of different network topologies, including traditional Multilayer Perceptron (MLP) networks, Bridged Multilayer Perceptron (BMLP) networks, and Fully Connected Cascade (FCC) networks, are evaluated by both theoretical analysis and experimental results; third, the generalization issue is discussed to illustrate the importance of choosing the proper size of neural networks.

Efficiency Optimization of a DSP-Based Standalone PV System Using Fuzzy Logic and Dual-MPPT Control

Abstract: This paper presents a new digital control scheme for a standalone photovoltaic (PV) system using fuzzy-logic and a dual maximum power point tracking (MPPT) controller. The first MPPT controller is an astronomical two-axis sun tracker, which is designed to track the sun over both the azimuth and elevation angles and obtain maximum solar radiation at all times. The second MPPT algorithm controls the power converter between the PV panel and the load and implements a new fuzzy-logic (FLC)-based perturb and observe (P&O) scheme to keep the system power operating point at its maximum. The FLC-MPPT is based on a voltage control approach of the power converter with a discrete PI controller to adapt the duty cycle. The input reference voltage is adaptively perturbed with variable steps until the maximum power is reached. The proposed control scheme achieves stable operation in the entire region of the PV panel and eliminates therefore the resulting oscillations around the maximum power operating point. A 150-Watt prototype system is used with two TMS320F28335 eZdsp boards to validate the proposed control scheme performance.

Control Issues in Single-Stage Photovoltaic Systems: MPPT, Current and Voltage Control

Abstract: Photovoltaic Systems (PVS) can be easily integrated in residential buildings hence they will be the main responsible of making low-voltage grid power flow bidirectional. Control issues on both the PV side and on the grid side have received much attention from manufacturers, competing for efficiency and low distortion and academia proposing new ideas soon become state-of-the-art. This paper aims at reviewing part of these topics (MPPT, current and voltage control) leaving to a future paper to complete the scenario. Implementation issues on Digital Signal Processor (DSP), the mandatory choice in this market segment, are discussed.

Digital Control of Power Converters—A Survey

Abstract: Power converters offer a high capability to efficiently manage electrical energy flows. Until a few years ago, their primary use was in supplying motors in industrial applications and in electric traction systems. Nowadays, in addition to those fields they are employed in a very wide range of low, medium, and high power applications including residential applications, renewable energy systems, distributed generation, and automotive. Since digital control represents a key element of modern power converters, this paper presents a review on digital devices [microcontrollers, Field Programmable Gate Arrays (FPGA)], hardware and software design techniques as well as implementation issues useful for designing modern high-performance power converters.

Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization

Abstract: In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.

Robust Model Predictive Current Control of Three-Phase Voltage Source PWM Rectifier With Online Disturbance Observation

Abstract: This paper presents a robust model predictive current controller with a disturbance observer (DO-MPC) for three-phase voltage source PWM rectifier. The new algorithm is operated with constant switching frequency (CF-MPC). In order to minimize instantaneous d- and q-axes current errors in every sampling period, CF-MPC is implemented by selecting appropriate voltage vector sequence and calculating duty cycles. The fundamental of this algorithm is discussed and the instantaneous variation rates of d- and q-axes currents are deduced when each converter voltage vector is applied in six different sectors. A Luenberger observer is constructed for parameter mismatch and model uncertainty which affect the performance of the MPC. The gains of the disturbance observer are determined by root-locus analysis. Moreover, the stability of the disturbance observer is analyzed when there are errors in the inductor filter parameter. The proposed method has an inherent rapid dynamic response as a result of the MPC controller, as well as robust control performance with respect to the disturbance due to use of the combined observation algorithm. Simulation and experimental results on a 1.1 kW VSR are conducted to validate the effectiveness of the proposed solution.

Business Intelligence for Enterprise Systems: A Survey

Abstract: Business intelligence (BI) is the process of transforming raw data into useful information for more effective strategic, operational insights, and decision-making purposes so that it yields real business benefits. This new emerging technique can not only improve applications in enterprise systems and industrial informatics, respectively, but also play a very important role to bridge the connection between enterprise systems and industrial informatics. This paper was intended as a short introduction to BI with the emphasis on the fundamental algorithms and recent progress. In addition, we point out the challenges and opportunities to smoothly connect industrial informatics to enterprise systems for BI research.

Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System

Abstract: In this paper, the speed regulation problem for permanent magnet synchronous motor (PMSM) system under vector control framework is studied. First, a speed regulation scheme based on standard internal model control (IMC) method is designed. For the speed loop, a standard internal model controller is first designed based on a first-order model of PMSM by analyzing the relationship between reference quadrature axis current and speed. For the two current loops, PI algorithms are employed respectively. Second, considering the disadvantages that the standard IMC method is sensitive to control input saturation and may lead to poor speed tracking and load disturbance rejection performances, a modified IMC scheme is developed based on a two-port IMC method, where a feedback control term is added to form a composite control structure. Third, considering the case of large variations of load inertia, two adaptive IMC schemes with two different adaptive laws are proposed. A method based on disturbance observer is adopted to identify the inertia of PMSM and its load. Then a linear adaptive law is developed by analyzing the relationship between the internal model and identified inertia. Considering the control input saturation in practical applications, a fuzzy adaptive law based IMC scheme is developed based on apriori experimental tests and experiences, where a fuzzy inferencer based supervisor is designed to automatically tune the parameter of speed controller according to the identified inertia. The effectiveness of the proposed methods have been verified by Matlab simulation and TMS320F2808 DSP experimental results.

Dynamic Bayesian Networks for Vehicle Classification in Video

Abstract: Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle. In this paper, we present a stochastic multiclass vehicle classification system which classifies a vehicle (given its direct rear-side view) into one of four classes: sedan, pickup truck, SUV/minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector. The feature vector is then processed by a hybrid dynamic Bayesian network to classify each vehicle. Results are shown on a database of 169 videos for four classes.

Identification and Learning Control of Ocean Surface Ship Using Neural Networks

Abstract: This paper presents the problems of accurate identification and learning control of ocean surface ship in uncertain dynamical environments. Thanks to the universal approximation capabilities, radial basis function neural networks (NNs) are employed to approximate the unknown ocean surface ship dynamics. A stable adaptive NN tracking controller is first designed using backstepping and Lyapunov synthesis. Partial persistent excitation (PE) condition of some internal signals in the closed-loop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN controller is shown to be capable of accurate identification/learning of the uncertain ship dynamics in the stable control process. Subsequently, a novel NN learning control method which effectively utilizes the learned knowledge without re-adapting to the unknown ship dynamics is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

Abstract: This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.

AutoAssem: An Automated Assembly Planning System for Complex Products

Abstract: To automate assembly planning for complex products such as aircraft components, an assembly planning and simulation system called AutoAssem has been developed. In this paper, its system architecture is presented; the main components and the key technologies in each component are discussed. The core functions of the system that have been focused include Digital Assembly Modeling, Assembly Sequence Planning (ASP), Path Planning, Visualization, and Simulation. In contrast to existing assembly planning systems, one of the novelties of the system is it allows the assembly plans be automatically generated from a CAD assembly model with minimal manual interventions. Within the system, new methodologies have been developed to: (i) create Assembly Relationship Matrices; (ii) plan assembly sequences; (iii) generate assembly paths; and (iv) visualize and simulate assembly plans. To illustrate the application of the system, the assembly of a worm gear reducer is used as an example throughout this paper for demonstration purpose. AutoAssem has been successfully applied to virtual assembly design for various complex products so far.

Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks

Abstract: The problem of data sampling and collection in wireless sensor networks (WSNs) is becoming critical as larger networks are being deployed. Increasing network size poses significant data collection challenges, for what concerns sampling and transmission coordination as well as network lifetime. To tackle these problems, in-network compression techniques without centralized coordination are becoming important solutions to extend lifetime. In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is used for monitoring (e.g., building, industry, etc.). We propose a new algorithm for in-network compression aiming at longer network lifetime. Our approach is fully distributed: each node autonomously takes a decision about the compression and forwarding scheme to minimize the number of packets to transmit. Performance is investigated with respect to network size using datasets gathered by a real-life deployment. An enhanced version of the algorithm is also introduced to take into account the energy spent in compression. Experiments demonstrate that the approach helps finding an optimal tradeoff between the energy spent in transmission and data compression.

Computer-Controlled Variable Structure Systems: The State-of-the-Art

Abstract: Variable Structure Systems (VSSs) have been studied extensively for over 60 years and widely used in practical applications. A particular interest in VSS is the so-called Sliding-Mode Control (SMC), which is simple in control design and robust in parameter variations and disturbances. Modern control systems nowadays are implemented through computers. This presents challenges for SMC based VSS because the digital nature of computer-control weakens the essential assumption of SMC, that is, the switching frequency should be unlimited in order to deliver effective disruptive control actions. Extensive research activities have been since undertaken in computer-controlled VSS over the last thirty years. This survey provides a comprehensive account of the key developments in this field and examines the key technical research challenges for the future developments.

A Fusion Approach for Efficient Human Skin Detection

Abstract: A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.

Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia

Abstract: In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

Enhancing Real-Time Delivery of Gradient Routing for Industrial Wireless Sensor Networks

Abstract: This paper proposes gradient routing with two-hop information for industrial wireless sensor networks to enhance real-time performance with energy efficiency. Two-hop information routing is adopted from the two-hop velocity-based routing, and the proposed routing algorithm is based on the number of hops to the sink instead of distance. Additionally, an acknowledgment control scheme reduces energy consumption and computational complexity. The simulation results show a reduction in end-to-end delay and enhanced energy efficiency.

Evolutionary Pinning Control and Its Application in UAV Coordination

Abstract: Maximizing the controllability of complex networks by selecting appropriate nodes and designing suitable control gains is an effective way to control distributed complex networks. In this paper, some novel particle swarm optimization (PSO) approaches are developed to enhance the controllability of distributed networks. The proposed PSO algorithm is combined with a global search scheme and a modified simulated binary crossover (MSBX). In addition, the node importance-based method is introduced to study the controllability of distributed complex networks. A set of experiments show that the PSO with the global search and the MSBX (PSO-GSBX) can outperform some well-known evolutionary algorithms and pinning schemes. Following the PSO-GSBX approach, some interesting findings about pinned nodes, coupling strengths and the eigenvalues for enhancing the controllability of distributed networks are revealed. The obtained results and methods are applied in unmanned aerial vehicle (UAV) coordination to show their effectiveness. These findings will help to understand controllability of complex networks and can be applied in control science and industrial system.

RDDS: A Real-Time Data Distribution Service for Cyber-Physical Systems

Abstract: One of the primary requirements in many cyber-physical systems (CPS) is that the sensor data derived from the physical world should be disseminated in a timely and reliable manner to all interested collaborative entities. However, providing reliable and timely data dissemination services is especially challenging for CPS since they often operate in highly unpredictable environments. Existing network middleware has limitations in providing such services. In this paper, we present a novel publish/subscribe-based middleware architecture called Real-time Data Distribution Service (RDDS). In particular, we focus on two mechanisms of RDDS that enable timely and reliable sensor data dissemination under highly unpredictable CPS environments. First, we discuss the semantics-aware communication mechanism of RDDS that not only reduces the computation and communication overhead, but also enables the subscribers to access data in a timely and reliable manner when the network is slow or unstable. Further, we extend the semantics-aware communication mechanism to achieve robustness against unpredictable workloads by integrating a control-theoretic feedback controller at the publishers and a queueing-theoretic predictor at the subscribers. This integrated control loop provides Quality-of-Service (QoS) guarantees by dynamically adjusting the accuracy of the sensor models. We demonstrate the viability of the proposed approach by implementing a prototype of RDDS. The evaluation results show that, compared to baseline approaches, RDDS achieves highly efficient and reliable sensor data dissemination as well as robustness against unpredictable workloads.

An Adaptive Speed Sensorless Induction Motor Drive With Artificial Neural Network for Stability Enhancement

Abstract: An artificial neural network (ANN) based adaptive estimator is presented in this paper for the estimation of rotor speed in a sensorless vector-controlled induction motor (IM) drive. The model reference adaptive system (MRAS) is formed with instantaneous and steady state reactive power. Selection of reactive power as the functional candidate in MRAS automatically makes the system immune to the variation of stator resistance. Such adaptive system performs satisfactorily at very low speed. However, it is observed that an unstable region exists in the speed-torque domain during regeneration. In this work, ANN is applied to overcome such stability related problem. The proposed method is validated through computer simulation using MATLAB/SIMULINK. Sample results from a laboratory prototype (using dSPACE-1104) have confirmed the usefulness of the proposed estimator.

Multichannel Superframe Scheduling for IEEE 802.15.4 Industrial Wireless Sensor Networks

Abstract: The IEEE 802.15.4 protocol offers great potential for industrial wireless sensor networks, especially when operating in beacon-enabled mode over star or cluster-tree topologies. However, it is known that beacon collisions can undermine the reliability of cluster-tree networks, causing loss of synchronization between nodes and their coordinator. For this reason, this paper proposes a Multichannel Superframe Scheduling (MSS) algorithm, a novel technique that avoids beacon collisions by scheduling superframes over different radio channels, while maintaining the connectivity of all the clusters. The paper describes the MSS algorithm and addresses the advantages it provides over the time-division superframe scheduling. Analytical results are shown which provide a quantitative estimation of how the schedulability space is improved, while simulation results show that the proposed technique increases the number of schedulable clusters and the maximum cluster density. Finally, this paper proves the feasibility of the proposed approach describing a working implementation based on TinyOS.

A Distributed Topology Control Technique for Low Interference and Energy Efficiency in Wireless Sensor Networks

Abstract: Topology control plays an important role in the design of wireless ad hoc and sensor networks; it is capable of constructing networks that have desirable characteristics such as sparser connectivity, lower transmission power, and a smaller node degree. In this research, a new distributed topology control technique is presented that enhances energy efficiency and reduces radio interference in wireless sensor networks. Each node in the network makes local decisions about its transmission power and the culmination of these local decisions produces a network topology that preserves global connectivity. Central to this topology control technique is the novel Smart Boundary Yao Gabriel Graph (SBYaoGG) and optimizations to ensure that all links in the network are symmetric and energy efficient. Simulation results are presented demonstrating the effectiveness of this new technique as compared to other approaches to topology control.

Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination

Abstract: The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.

3-D Posture and Gesture Recognition for Interactivity in Smart Spaces

Abstract: Automatic perception of human posture and gesture from vision input has an important role in developing intelligent video systems. In this paper, we present a novel gesture recognition approach for human computer interactivity based on marker-less upper body pose tracking in 3-D with multiple cameras. To achieve the robustness and real-time performance required for practical applications, the idea is to break the exponentially large search problem of upper body pose into two steps: first, the 3-D movements of upper body extremities (i.e., head and hands) are tracked. Then using knowledge of upper body model constraints, these extremities movements are used to infer the whole 3-D upper body motion as an inverse kinematics problem. Since the head and hand regions are typically well defined and undergo less occlusion, tracking is more reliable and could enable more robust upper body pose determination. Moreover, by breaking the problem of upper body pose tracking into two steps, the complexity is reduced considerably. Using pose tracking output, the gesture recognition is then done based on longest common subsequence similarity measurement of upper body joint angles dynamics. In our experiment, we provide an extensive validation of the proposed upper body pose tracking from 3-D extremity movement which showed good results with various subjects in different environments. Regarding the gesture recognition based on joint angles dynamics, our experimental evaluation of five subjects doing six upper body gestures with average classification accuracies over 90% indicates the promise and feasibility of the proposed system.

Using Propositional Logic for Requirements Verification of Service Workflow

Abstract: This paper presents a requirement-oriented automated framework for formal verification of service workflows. It is based on our previous work describing the requirement-oriented service workflow specification language called SWSpec. This language has been developed to facilitate workflow composer as well as arbitrary services willing to participate in a workflow to formally and uniformly impose their own requirements. As such, SWSpec provides a formal way to regulate and control workflows. The key component of the to-be-proposed framework centers on verification algorithms that rely on propositional logic. We demonstrate that logic-based workflow verification can be applied to SWSpec which is capable of checking compliance and also detecting conflicts of the imposed requirements. By automating compliance checking process, this framework will support scalable services interoperation in the form of workflows in opened environments.

Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems

Abstract: This paper presents a novel adaptive Gravitational Search Algorithm (GSA) for the optimal tuning of fuzzy controlled servo systems characterized by second-order models with an integral component and variable parameters. The objective functions consist of the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The proposed adaptive GSA solves the optimization problems resulting in a new generation of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) with a reduced time constant sensitivity. A design method for T-S PI-FCs is then proposed and experimentally validated in the representative case study of the optimal tuning of T-S PI-FCs for the position control system of a servo system.

Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks

Abstract: This paper presents new results on a neural network approach to nonlinear model predictive control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The unknown higher-order term resulted from the decomposition, together with the unmodeled dynamics of the original plant, are modeled by using a feedforward neural network via supervised learning. The optimization problem for nonlinear model predictive control is then formulated as a quadratic programming problem based on successive Jacobian linearization about varying operating points and iteratively solved by using a recurrent neural network called the simplified dual network. Simulation results are included to substantiate the effectiveness and illustrate the performance of the proposed approach.

SWSpec: The Requirements Specification Language in Service Workflow Environments

Abstract: Advanced technologies have changed the nature of business processes in the form of services. In coordinating services to achieve a particular objective, service workflow is used to control service composition, execution sequences as well as path selection. Since existing mechanisms are insufficient for addressing the diversity and dynamicity of the requirements in a large-scale distributed environment, developing formal requirements specification is necessary. In this paper, we propose a Service Workflow Specification language, called SWSpec, which allows arbitrary services in a workflow to formally and uniformly impose their requirements. As such, the solution will provide a formal way to regulate and control workflows as well as enrich the proliferation of service provisions and consumptions in opened environments.