International audience

Text semantic hashing maps a text to a compact binary code, which is an important part of information retrieval and language processing. There are two main challenges for this task, one is to make the hash codes express the hierarchical category information for improving the retrieval accuracy, and the other is how to deal with the hard samples. In this paper, we adopt the Bernoulli VAE to encode the text semantics and design the parent and child level contrastive losses to learn the hierarchical information of the text. To find the hard samples, for each category, we introduce a latent sphere space to split the majority samples and hard samples, where the center and radius are dynamically calculated based on the semantic distance between samples. For the hard samples, we introduce the de-confusion loss to pull them close to the center. We conduct experiments on three datasets and the results show that the proposed model outperforms the SOTA baselines. The ablation experiments show that the category constraints and the de-confusion loss contribute to the model performance. The results of t-SNE also show that the hash codes learned by our model reflect high category differences.

The deep operator networks (DON), a class of neural operators that learn mappings between function spaces, have recently emerged as surrogate models for parametric partial differential equations (PDEs). However, their full potential for accurately approximating general black-box PDEs remains underexplored due to challenges in training stability and performance, primarily arising from difficulties in learning mappings between low-dimensional inputs and high-dimensional outputs. Furthermore, inadequate encoding of input functions and query positions limits the generalization ability of DONs. To address these challenges, we propose the Dynamical Coupled Operator (DCO), which incorporates temporal dynamics to learn coupled functions, reducing information loss and improving training robustness. Additionally, we introduce an adaptive spectral input function encoder based on empirical mode decomposition to enhance input function representation, as well as a hybrid location encoder to improve query location encoding. We provide theoretical guarantees on the universal expressiveness of DCO, ensuring its applicability to a wide range of PDE problems. Extensive experiments on real-world, high-dimensional PDE datasets demonstrate that DCO significantly outperforms DONs.

This paper presents an ultra-low power 915 MHz Local Oscillator (LO) with adaptive frequency calibration algorithm for IoT Wake-Up Receivers (WURs). The proposed algorithm can adaptively adjust and minimize the calibration time of each bit oscillator tuning word (OTW) as well as the number of OTWs that require re-calibration after open-loop time. These two features help reducing the average LO power consumption by minimizing the closed-loop frequency calibration time. The duty cycle of the closed-loop time can also be adjusted flexibly to further reduce the average LO power consumption at a given frequency stability requirement. Fabricated in 40-nm CMOS, this proposed algorithm reduces the frequency calibration time from 108 μs to 42 μs, achieving a time reduction of 61%. An average LO power consumption of 93 μW at a 5% closed-loop time duty cycle is measured with a frequency accuracy of 120 ppm.

UI task automation enables efficient task execution by simulating human interactions with GUIs, without modifying the existing application code. However, its broader adoption is constrained by the need for expertise in both scripting languages and workflow design. To address this challenge, we present Prompt2Task, a system designed to comprehend various task-related textual prompts (e.g., goals, procedures), thereby generating and performing the corresponding automation tasks. Prompt2Task incorporates a suite of intelligent agents that mimic human cognitive functions, specializing in interpreting user intent, managing external information for task generation, and executing operations on smartphones. The agents can learn from user feedback and continuously improve their performance based on the accumulated knowledge. Experimental results indicated a performance jump from a 22.28% success rate in the baseline to 95.24% with Prompt2Task, requiring an average of 0.69 user interventions for each new task. Prompt2Task presents promising applications in fields such as tutorial creation, smart assistance, and customer service.

The ultimate goal of multi-objective optimization (MO) is to assist human decision-makers (DMs) in identifying solutions of interest (SOI) that optimally reconcile multiple objectives according to their preferences. Preference-based evolutionary MO (PBEMO) has emerged as a promising framework that progressively approximates SOI by involving human in the optimization-cum-decision-making process. Yet, current PBEMO approaches are prone to be inefficient and misaligned with the DM’s true aspirations, especially when inadvertently exploiting mis-calibrated reward models. This is further exacerbated when considering the stochastic nature of human feedback. This paper proposes a novel framework that navigates MO to SOI by directly leveraging human feedback without being restricted by a predefined reward model nor cumbersome model selection. Specifically, we developed a clustering-based stochastic dueling bandits algorithm that strategically scales well to high-dimensional dueling bandits, and achieves a regret of $\mathcal{O}(K^2\log T)$, where $K$ is the number of clusters and $T$ is the number of rounds. The learned preferences are then transformed into a unified probabilistic format that can be readily adapted to prevalent EMO algorithms. This also leads to a principled termination criterion that strategically manages human cognitive loads and computational budget. Experiments on $48$ benchmark test problems, including synthetic problems, RNA inverse design and protein structure prediction, fully demonstrate the effectiveness of our proposed approach.

Newton's Second Law of Motion algorithm is crucial to interactive visual effects and interactive behavior in interface design. Designers can only utilize simple algorithm templates in interface design since they lack organized mathematical science, especially programming. Directly using Newton's Second Law of Motion algorithm introduces two interface design issues. First, the created picture has a simplistic impact, laborious interaction, too few interactive parts, and boring visual effects. Second, using this novel approach directly to interface design reduces creativity, originality, and cognitive inertia. This study suggests a Newton's Second Law–based algorithm modification. It provides a novel algorithm application idea and a design strategy based on algorithm change to enable new interface design. Algorithm design gives interface design a new viewpoint and improves content production. In the arithmetic process of Newton's Second Law of Motion algorithm, the introduction of repulsive force, reset force, shape, color, and other attributes of interactive objects, and the integration of other algorithms to transform its basic arithmetic logic, is conducive to the improvement of the visual effect of interaction design. It also improves users’ interaction experiences, sentiments, and desire to participate with design work.

Efficient neural network training is essential for in situ training of edge artificial intelligence (AI) and carbon footprint reduction in general. Train neural network on the edge is challenging because there is a large gap between limited resources on edge and the resource requirement of current training methods. Existing training methods are based on the assumption that the underlying computing infrastructure has sufficient memory and energy supplies. These methods involve two copies of the model parameters, which is usually beyond the capacity of on-chip memory in processors. The data movement between off-chip and on-chip memory incurs large amounts of energy. We propose resource constrained training (RCT) to realize resource-efficient training for edge devices and servers. RCT only keeps a quantized model throughout the training so that the memory requirement for model parameters in training is reduced. It adjusts per-layer bitwidth dynamically to save energy when a model can learn effectively with lower precision. We carry out experiments with representative models and tasks in image classification, natural language processing, and crowd counting applications. Experiments show that on average, 8-15-bit weight update is sufficient for achieving SOTA performance in these applications. RCT saves 63.5%-80% memory for model parameters and saves more energy for communications. Through experiments, we observe that the common practice on the first/last layer in model compression does not apply to efficient training. Also, interestingly, the more challenging a dataset is, the lower bitwidth is required for efficient training.

Traditional evolutionary multi-objective optimization (EMO) algorithm is to generate a set of non-dominated solutions on the Pareto front (PF). However, this technique falls short of delivering the outcomes for multi-objective optimization problems (MOPs) containing user preference. In this paper, we present a novel EMO algorithm that incorporates user preferences via a decision maker (DM). Our approach comprises three modules: consultation, preference elicitation and optimization. The DM undertakes the consultation and preference elicitation using Gaussian process (GP) to provide preference information. We employ the decomposition-based EMO algorithm (i.e., MOEA/D) for optimization. The experiment comprises two sessions. Firstly, we simulate the decision maker module with GP. Secondly, we simulate our proposed method and compare its performance with existing interactive optimization algorithms. Our research proposes a new preference-based EMO algorithm that addresses the shortcomings of traditional techniques and unlocks new possibilities for multi-objective optimization.

Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency. Specialized hardware implementations of those neurons clearly have advantages over general-purpose devices in terms of power and performance, but exhibit poor scalability when it comes to accelerating large neural networks. DeepFire2 introduces a hardware architecture which can map large network layers efficiently across multiple super logic regions in a multi-die FPGA. That gives more control over resource allocation and parallelism, benefiting both throughput and energy consumption. Avoiding the use of lookup tables to implement theANDoperations of an SNN, prevents the layer size to be limited by logic resources. A deep pipeline does not only lead to an increased clock speed of up to 600 MHz. We double the throughput and power efficiency compared to our previous version of DeepFire, which equates to an almost 10-fold improvement over other previous implementations. Importantly, we are able to deploy a large ImageNet model, while maintaining a throughput of over 1500 frames per second.

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.

Using voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user’s input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI.

As the first successful attempt to combine deep neural network and reinforcement learning, Deep Q-learning Network (DQN) draws a lot of attention from reinforcement learning researchers. One of the most important components of DQN is target network, which is used to stabilize learning process. When confront complex network structure, the existence of target network means extra memory resource to preserve the neural network weights and high computing cost to calculate target. Thus, we propose a Deep Hybrid Q-learning Network (DHQN) algorithm, which introduces an alternative approach, Random Hybrid Optimization (RHO), that can simplify DQN and attain a more stable and faster learning without a target network. We illustrate that RHO can decelerate divergence in the classical off-policy counterexample θ → 2θ problem. We also testify the effectiveness of DHQN in several control and Atari domains, which shows DHQN outperforms DQN without a target network and original DQN.

Learn in-situ is a growing trend for Edge AI. Training deep neural network (DNN) on edge devices is challenging because both energy and memory are constrained. Low precision training helps to reduce the energy cost of a single training iteration, but that does not necessarily translate to energy savings for the whole training process, because low precision could slows down the convergence rate. One evidence is that most works for low precision training keep an fp32 copy of the model during training, which in turn imposes memory requirements on edge devices. In this work we propose Adaptive Precision Training. It is able to save both total training energy cost and memory usage at the same time. We use model of the same precision for both forward and backward pass in order to reduce memory usage for training. Through evaluating the progress of training, APT allocates layer-wise precision dynamically so that the model learns quicker for longer time. APT provides an application specific hyper-parameter for users to play trade-off between training energy cost, memory usage and accuracy. Experiment shows that APT achieves more than 50% saving on training energy and memory usage with limited accuracy loss. 20% more savings of training energy and memory usage can be achieved in return for a 1% sacrifice in accuracy loss.

More massive volume of data are generated in many areas than ever before. However, the missing of some values in collected data always occurs in practice and challenges extracting maximal value from these large scale data sets. Nevertheless, in multivariable time series, most of the existing methods either might be infeasible or could be inefficient to predict the missing data. In this paper, we have taken up the challenge of missing data prediction in multivariable time series by employing improved matrix factorization techniques. Our approaches are optimally designed to largely utilize both the internal patterns of each time series and the information of time series across multiple sources. Based on the idea, we have imposed three different regularization terms to constrain the objective functions of matrix factorization and built five corresponding models. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction in multivariable time series. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large scale multivariable time series.

As a major strategy to ensure the safety of IT infrastructure, anomaly detection plays a more important role in cloud computing platform which hosts the entire applications and data. On top of the classic Markov chain model, we proposed in this paper a feasible multi-order Markov chain based framework for anomaly detection. In this approach, both the high-order Markov chain and multivariate time series are adopted to compose a scheme described in algorithms along with the training procedure in the form of statistical learning framework. To curb time and space complexity, the algorithms are designed and implemented with non-zero value table and logarithm values in initial and transition matrices. For validation, the series of system calls and the corresponding return values are extracted from classic Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set to form a two-dimensional test input set. The testing results show that the multi-order approach is able to produce more effective indicators: in addition to the absolute values given by an individual single-order model, the changes in ranking positions of outputs from different-order ones also correlate closely with abnormal behaviours.

The fast multipole method (FMM) is a promising mathematical technique that accelerates the calculation of long-ranged forces in the large-sized n-body problem. Existing implementations of the FMM on general-purpose processors are energy and resource inefficient. To mitigate these issues, we propose a hardware pipeline that accelerates three key FMM steps. The pipeline improves energy efficiency by exploiting fine-granularity parallelism of the FMM. We reuse the pipeline for different FMM steps to reduce resource usage by 66%. Compared to the state-of-the-art implementations on CPUs and GPUs, our implementation requires 15% less energy and delivers 2.61 times more floating-point operations.

Drawing on screw theory and the virtual joint method, this paper presents a general and hierarchical approach for semianalytical stiffness modeling of parallel mechanisms. The stiffness model is built by two essential steps: 1) formulating the map between the stiffness matrices of platform and limbs using the duality of wrench and twist of the platform; and 2) formulating the map between stiffness matrices of a limb and a number of elastic elements in that limb using the duality of the wrench attributed to the limb and the twist of the endlink of that limb. By merging these two threads, the Cartesian stiffness matrix can be explicitly expressed in terms of the compliance matrices of joints and links. The proposed approach bridges the gap between two currently available approaches and is thereby very useful for evaluating stiffness over the entire workspace and investigating the influences of joint/link compliances on those of the platform in a quick and precise manner. A stiffness analysis for a 3-PRS parallel mechanism is presented as an example to illustrate the effectiveness of the proposed approach.

In the workflow of SKA-SDP (Square Kilometer Array Radio Telescope-Scientific Data Processing), FFT (Fast Fourier Transform) calculation takes a significant proportion of computation overhead. Moreover, FFT computation has to be done within the tight power budget, which existing generic high performance computing architectures cannot meet. To explore power efficiency of FFT computation, this study is designed with initial evaluation of FFT implementation in variable precision on Xilinx ML605 FPGA (field programmable gate array). The FPGA-based implementation of FFT includes a Xilinx IP Core version 7.1, which supports fixed-point and floating-point computation in single precision. The input data width and phase factor width of fixed-point computation can be varied from 8 bits to 34 bits, allowing that calculation accuracy can be adjusted by setting the width. Since single precision is redundant to accuracy requirement of SKA-SDP, fixed-point calculation is designed to emulate single-precision floating point computation. Calculational power dissipation and throughput with different phase factors on FPGA was measured respectively. The final result demonstrates that the implementation on FPGA ensures sufficient precision at a much less power cost compared with floating-point FFT. In other words, this study indicates variable precision computation would be an efficient way to improve power efficiency.

In the era of cloud computing, data centers are well-known to be bounded by the power wall issue. This issue lowers the profit of service providers and obstructs the expansions of data center's scale. As virtual machine's behavior was not explored sufficiently in classic data center's power-saving strategies, in this paper we address the power consumption issue in the setting of a virtualized data center. We propose an efficient power-aware resource scheduling strategy that reduces data center's power consumption effectively based on VM live migration which is a key technical feature of cloud computing. Our scheduling algorithm leverages the Xen platform and consolidates VM workloads periodically to reduce the number of running servers. To satisfy each VM's service level agreements, our strategy keeps adjusting VM placements between scheduling rounds. We developed a power-aware data center simulator to test our algorithm. The simulator runs in time domain and includes server's segmented linear power model. We validated our simulator using measured server power trace. Our simulation shows that compared with event-driven schedulers, our strategy improves data center power budget by 35% for random workloads resembling web-requests, and improve data center power budget by 22.7% for workloads exhibiting stable resource requirements like ScaLAPACK.

Cloud computing operators provide flexible, convenient, and affordable means to access public and private services. Virtual machine (VM) live migration, as an important feature of virtualization technique in cloud computing, ensures high efficiency and performance of computing infrastructure, while it stays transparent to clients. However, VM live migration is observed to cover anomalies due to their statistical similarity. To tackle the critical security issue, in this work, we propose an intelligent scheme to mine statistical data from cloud infrastructure to detect anomalies even if VMs are migrated to a new host with different infrastructure settings. In addition to detection of the existence of anomalies, our scheme is capable of identifying the possible sources of anomalies, which gives administrators clues to pinpoint and clear the anomalies.

Cloud computing, a delivery of computing as a service mainly implying how to use utilities in our context, can be provided either at infrastructure, platform or software levels. The Desktop-as-a-Service (DaaS) paradigm, derives from the software level Software-as-a-Service (SaaS) paradigm, is drawing increasing interest because of its transformation from desktops into a cost-efficient, scalable and comfortable subscription service. Unlike most existing solutions delivering service with various protocols based on image transmitting in PC dominating environment, we present a DaaS with cloud server technologies on FPGA to address the problem of high power consumption and heavy network traffic. With the booming of mobile cloud computing, users can access the service on demand with smart phones or other portable devices like iPad or Amazon kindle as well as PC. Our system provides virtual desktop web pages written in HTML/JavaScript to avoid frequent image transmissions and reduce network traffic. To build the cloud prototype system, we combine Lightweight TCP/IP stack (LwIP) and Java Optimized Processor (JOP) to build a web server enabling dynamic web page interactions. Our system significantly saves volumes of data in transmission and network bandwidth. Analytical performance evaluation shows that on average, our system suffers only 25% transmitting latency and saves 46% of energy efficiency in comparison to other solutions. Our efficient DaaS based on FPGA explores new application of embedded web server in green cloud computing as well as new service paradigm of mobile cloud computing.

Due to power wall and slow performance improvement in a single core micro-architecture, multiple even many cores based processors rose as the main stream processor. Nevertheless, thermal threats regarding reliability and lifetime of processors are still among the major concerns which received much attention in terms of algorithms and hardware design to reduce processor temperature and keep application performance in recent years. In this paper, we propose and implement a thermal-aware Round-Robin scheduling algorithm for process migration in the Linux environment on a quad-core processor. Bearing designer's goals in mind, such as performance, load-balancing, and reliability, we managed to achieve much bigger temperature fall than previous results of Round-Robin scheduler on a dual-core processor as well as baseline Linux scheduler on a quad-core processor. Moreover, the performance loss due to scheduling overhead is modest in our approach. Our results indicate that thermal-aware scheduling is a valid approach to tackling thermal issues on multi-core processors. There will be increasing demand for thermal-aware scheduling as the number of cores on a single processor increases.

This paper presents a general and systematic approach to formulate the dimensionally homogeneous Jacobian, which is an important issue for the dexterity evaluation and dimensional synthesis off-degrees-of-freedom (DOF) (f≤ 6) parallel manipulators having mixed rotational and translational movement capabilities. By the utilization offindependent coordinates to describe the specified motion types of the platform, thef×fdimensionally homogeneous Jacobian is derived directly from the generalized Jacobian, provided that the manipulator has only one type of actuator. The condition number of the new Jacobian is then employed to evaluate the dexterity of two typical 3-DOF parallel manipulators as an illustration of the effectiveness of this approach.

This paper deals with the kinematic calibration of a 3-DOF parallel mechanism using a double-ball-bar (DBB) system. The mechanism forms the main body of a 5-DOF reconfigurable hybrid robot named TriVariant that is a modified version of the Tricept, achieved by integrating one of its three active limbs into the passive one. The first order error mapping function is formulated to link the measured data and the geometric source errors affecting the compensatable pose accuracy. Issues to enhance the accuracy and efficiency of the calibration are investigated. The results are employed to a prototype machine calibration

This paper deals with the finite element analysis (FEA) of two 5-DOF reconfigurable hybrid robots, the Tricept and the TriVariant, by means of ANSYS parametric design language (APDL). The research interests are focused on: (1) precise FEA formulation of different types of joints by setting of contact elements, and (2) the development of an effective modeling strategy for the rapid stiffness and natural frequencies estimation associated with different configurations. The computational results show that two robots are of similar static and dynamic performances provided that they have identical geometrical dimensions, elastical and inertial parameters

This paper deals with the tolerance design of a two-degree-of-freedom translational parallel robot module for high-speed pick-and-place operations. The module is an overconstrained parallel mechanism using two sets of parallelograms in each limb. A probabilistic model of the uncompensatable pose error is formulated, together with a compatibility condition to ensure the mobility of the robot. Based upon this model, optimization of the tolerances of the geometric source errors and joint clearances is conducted, subject to a set of appropriate constraints. Simulation and experimental results are given to demonstrate the effectiveness of this approach.

This paper deals with the conceptual design and dimensional synthesis of a 3-DOF parallel mechanism module which forms the main body of a newly invented 5-DOF reconfigurable hybrid robot named "TriVariant." The TriVariant is a modified version of the Tricept robot, achieved by integrating one of the three active limbs into the passive limb. The idea leading to the innovation of the module is systematically addressed. Its kinematic performance is optimized by minimizing a global and comprehensive conditioning index subject to a set of appropriate mechanical constraints. It is concluded that the proposed hybrid system is more cost-effective and has a competitive kinematic performance in comparison with the well-known Tricept robot.

Aimed at a 3-DoF reconfigurable parallel kinematic machine, a method for tuning PID parameters of its servo system is presented. At first, position, velocity and acceleration inverse solution models of machine driven by the outer translation pairs that contain parallelogram strut structures are developed, and the inverse dynamics model of the rigid body has been set up by means of virtual work principle. Based on the above, making the mean square error of the trajectory of the moving platform least as the optimum goal, and considering the real-timely variational inertial load along with its pose at the same time, the overshoot, adjusting time of the unit step response of the servo system and the optimum range of PID parameters are given through simulations. At last, the correctness and effectiveness of the method are proved by the experiments.

This paper presents a hybrid method for the optimum kinematic design of two-degree-of-freedom (2-DOF) parallel manipulators with mirror symmetrical geometry. By taking advantage of both local and global approaches, the proposed method can be implemented in two steps. In the first step, the optimal architecture, in terms of isotropy and the behavior of the direct Jacobian matrix, is achieved, resulting in a set of closed-form parametric relationships that enable the number of design variables to be reduced. In the second step, the workspace bounded by the specified conditioning index is generated, which allows only one design parameter to be determined by optimizing a comprehensive index in a rectangular workspace. The kinematic optimization of a revolute-jointed 2-DOF parallel robot has been taken as an example to illustrate the effectiveness of this approach.

This paper presents a mathematic proof for the identifiability of geometric parameters of 6-DOF parallel kinematic machines (PKM). Rank deficiency of the identification matrix is justified if the end-effector undergoes all possible degrees of freedom but only a set of position errors normal to a plane is measured. An approach is proposed to tackle this problem, which enables the full set of parameter errors to be identified by only using a minimum set of pose errors: 1) the "flatness" of a fictitious plane generated by the tip of endpoint sensor; 2) the "squareness" of two orthogonal axes; and 3) the orientation of the end-effector at the initial configuration. Consequently, the burden arisen from the orientation error measurement may be dramatically reduced. The proposed method is so general that it can also be used to handle the parameter identification problems of the PKM with fewer than six degrees of freedom.

A novel and plug-and-play robot wrist with three rotational degrees of freedom (DOF) is proposed in this paper. The wrist is composed of three independent kinematic chains, each of which rotates with respect to the fixed reference frame. Thus, the output of the wrist is the resultant of the differential motions of the three kinematic chains. The structure of the mechanism is simple and compact with a relatively large orientation capability. Various end-effectors, CCD camera for instance, can be mounted on the wrist through standard interface. The working principle and mechanical structure are described and the mathematical models for inverse and forward analyses are developed. The singularity of the wrist is also obtained.

Presents a simple yet comprehensive approach that enables the stiffness of a tripod-based parallel kinematic machine to be quickly estimated. The approach arises from the basic idea for the determination of the equivalent stiffness of a group of serially connected linear springs and can be implemented in two steps. In the first step, the machine structure is decomposed into two substructures associated with the machine frame and parallel mechanism. The stiffness models of these two substructures are formulated by means of the virtual work principle. This is followed by the second step that enables the stiffness model of the machine structure as a whole to be achieved via linear superposition. The three-dimensional representations of the machine stiffness within the usable workspace are depicted and the contributions of different component rigidities to the machine stiffness are discussed. The results are compared with those obtained through experiments.

This paper presents a simple yet comprehensive approach that enables the stiffness of a tripod-based parallel kinematic machine to be quickly estimated. The approach can be implemented by two steps. In the first step, the machine structure is decomposed into two substructures associated with the machine frame and the parallel mechanism. The stiffness model of each substructure is formulated by assuming that the components in the other substructure are rigid. This is followed by the second step that enables the stiffness model of the machine structure to be achieved by linear superposition. A 3D representation of the stiffness distributions within the usable workspace are depicted with comparison to those obtained through the finite element analysis.

In this paper, an effective approach to determine the carriage stroke of 6-PSS parallel manipulator is presented. The analytical expression of the position workspace boundary is formulated using differential geometry. This allows the closed form solution to the carriage stroke necessary to generate a cylindrical prescribed workspace to be determined.

A novel methodology to formulate the closed form solution to the orientation workspace of Gough-Stewart parallel manipulators is presented by using a fictitious four-bar spatial linkage model. The possible mechanical constraints are considered which include the strut length and the passive joint limitations. Several examples are given to illustrate the effectiveness of this approach.