Manufacturing robotics is moving towards human-robot collaboration with light duty robots being used side by side with workers. Similarly, exoskeletons that are both passive (spring and counterbalance forces) and active (motor forces) are worn by humans and used to move body parts. Exoskeletons are also called ‘wearable robots’ when they are actively controlled using a computer and integrated sensing. Safety standards now allow, through risk assessment, both manufacturing and wearable robots to be used. However, performance standards for both systems are still lacking. Ongoing research to develop standard test methods to assess the performance of manufacturing robots and emergency response robots can inspire similar test methods for exoskeletons. This paper describes recent research on performance standards for manufacturing robots as well as search and rescue robots. It also discusses how the performance of wearable robots could benefit from using the same test methods.
Cyber physical systems (CPSs) incorporate computation, communication, and physical processes. The deep coupling and continuous interaction between such processes lead to a significant increase in complexity in the design and implementation of CPSs. Consequently, whereas developing CPSs from scratch is inefficient, developing them with the aid of CPS run-time sup-porting platforms can be efficient. In recent years, much research has been actively conducted on CPS run-time supporting plat-forms. However, few surveys have been conducted on these platforms. In this paper, we analyze and evaluate existing CPS run-time supporting platforms by first classifying them into three categories from the viewpoint of software architecture: com-ponent-based platforms, service-based platforms, and agent-based platforms. Then, for each type, we detail its design philosophy, key technical problems, and corresponding solutions with specific use cases. Subsequently, we compare existing platforms from two aspects: construction approaches for CPS tasks and support for non-functional properties. Finally, we outline several im-portant future research issues.
We investigate the use of an approximation method for obtaining near-optimal solutions to a kind of nonlinear continuous-time (CT) system. The approach derived from the Galerkin approximation is used to solve the generalized Hamilton-Jacobi-Bellman (GHJB) equations. The Galerkin approximation with Legendre polynomials (GALP) for GHJB equations has not been applied to nonlinear CT systems. The proposed GALP method solves the GHJB equations in CT systems on some well-defined region of attraction. The integrals that need to be computed are much fewer due to the orthogonal properties of Legendre polynomials, which is a significant advantage of this approach. The stabilization and convergence properties with regard to the iterative variable have been proved. Numerical examples show that the update control laws converge to the optimal control for nonlinear CT systems.
A modeling method is proposed for a dynamic fast steering mirror (FSM) system with dual inputs and dual outputs. A physical model of the FSM system is derived based on first principles, describing the dynamics and coupling between the inputs and outputs of the FSM system. The physical model is then represented in a state-space form. Unknown parameters in the state-space model are identified by the subspace identification algorithm, based on the measured input-output data of the FSM system. The accuracy of the state-space model is evaluated by comparing the model estimates with measurements. The variance-accounted-for value of the state-space model is better than 97%, not only for the modeling data but also for the validation data set, indicating high accuracy of the model. Comparison is also made between the proposed dynamic model and the conventional static model, where improvement in model accuracy is clearly observed. The model identified by the proposed method can be used for optimal controller design for closed-loop FSM systems. The modeling method is also applicable to FSM systems with similar structures.
String similarity join (SSJ) is essential for many applications where near-duplicate objects need to be found. This paper targets SSJ with edit distance constraints. The existing algorithms usually adopt the filter-andrefine framework. They cannot catch the dissimilarity between string subsets, and do not fully exploit the statistics such as the frequencies of characters. We investigate to develop a partition-based algorithm by using such statistics. The frequency vectors are used to partition datasets into data chunks with dissimilarity between them being caught easily. A novel algorithm is designed to accelerate SSJ via the partitioned data. A new filter is proposed to leverage the statistics to avoid computing edit distances for a noticeable proportion of candidate pairs which survive the existing filters. Our algorithm outperforms alternative methods notably on real datasets.
Researchers across the globe have been increasingly interested in the manner in which important research topics evolve over time within the corpus of scientific literature. In a dataset of scientific articles, each document can be considered to comprise both the words of the document itself and its citations of other documents. In this paper, we propose a citation- content-latent Dirichlet allocation (LDA) topic discovery method that accounts for both document citation relations and the con-tent of the document itself via a probabilistic generative model. The citation-content-LDA topic model exploits a two-level topic model that includes the citation information for ‘father’ topics and text information for sub-topics. The model parameters are estimated by a collapsed Gibbs sampling algorithm. We also propose a topic evolution algorithm that runs in two steps: topic segmentation and topic dependency relation calculation. We have tested the proposed citation-content-LDA model and topic evolution algorithm on two online datasets, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) and IEEE Computer Society (CS), to demonstrate that our algorithm effectively discovers important topics and reflects the topic evolution of important research themes. According to our evaluation metrics, citation-content-LDA outperforms both content-LDA and citation-LDA.
The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm (FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimi-zation precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly’s smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.
The string-to-tree model is one of the most successful syntax-based statistical machine translation (SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model: (1) adding role labels in the syntax tree; (2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.
We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search (DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.
Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness.
As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.
User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms. These issues pose a great challenge for predicting trust relations and further building trust networks. In this study, we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework, bTrust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.
Dense network coding (NC) is widely used in wireless cooperative downloading systems. Wireless devices have limited computing resources. Researchers have recently found that dense NC is not suitable because of its high coding complexity, and it is necessary to use chunked NC in wireless environments. However, chunked NC can cause more communications, and the amount of communications is affected by the chunk size. Therefore, setting a suitable chunk size to improve the overall perfor-mance of chunked NC is a prerequisite for applying it in wireless cooperative downloading systems. Most of the existing studies on chunked NC focus on centralized wireless broadcasting systems, which are different from wireless cooperative downloading systems with distributed features. Accordingly, we study the performance of chunked NC based wireless cooperative downloading systems. First, an analysis model is established using a Markov process taking the distributed features into consideration, and then the block collection completion time of encoded blocks for cooperative downloading is optimized based on the analysis model. Furthermore, queuing theory is used to model the decoding process of the chunked NC. Combining queuing theory with the analysis model, the decoding completion time for cooperative downloading is optimized, and the optimal chunk size is derived. Numerical simulation shows that the block collection completion time and the decode completion time can be largely reduced after optimization.
We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram
For the primary mirror of a large-scale telescope, an electro-hydraulic position control system (EHPCS) is used in the primary mirror support system. The EHPCS helps the telescope improve imaging quality and requires a micron-level position control capability with a high convergence rate, high tracking accuracy, and stability over a wide mirror cell rotation region. In addition, the EHPCS parameters vary across different working conditions, thus rendering the system nonlinear. In this paper, we propose a robust closed-loop design for the position control system in a primary hydraulic support system. The control system is synthesized based on quantitative feedback theory. The parameter bounds are defined by system modeling and identified using the frequency response method. The proposed controller design achieves robust stability and a reference tracking performance by loop shaping in the frequency domain. Experiment results are included from the test rig for the primary mirror support system, showing the effectiveness of the proposed control design.
We propose a novel axis-symmetric modified hybrid permanent magnet (PM)/electromagnet (EM) magnetomotive force actuator for a variable valve timing camless engine. The design provides a large magnetic force with low energy consump-tion, low coil inductance, PM demagnetization isolation, and improved transient response. Simulation and experimental results confirm forces of about 200 N (in the presence of coil current) at the equilibrium position and 500 N (in the absence of coil current) at the armature seat. We compared our proposed design with a double solenoid valve actuator (DSVA). The finite element method (FEM) designs of the DSVA and our proposed valve actuator were validated by experiments performed on manufactured prototypes.
To simplify the process for identifying 12 types of symmetric variables in Boolean functions, we propose a new symmetry detection algorithm based on minterm expansion or the truth table. First, the order eigenvalue matrix based on a truth table is defined according to the symmetry definition of a logic variable. By analyzing the constraint conditions of the order eigenvalue matrix for 12 types of symmetric variables, an algorithm is proposed for identifying symmetric variables of the Boolean function. This algorithm can be applied to identify the symmetric variables of Boolean functions with or without don’t-care terms. The proposed method avoids the restriction by the number of logic variables of the graphical method, spectral coefficient methods, and AND-XOR expansion coefficient methods, and solves the problem of completeness in the fast compu-tation method. The algorithm has been implemented in C language and tested on MCNC91 benchmarks. The application results show that, compared with the traditional methods, the new algorithm is an optimal detection method in terms of the applicability of the number of logic variables, the Boolean function including don’t-care terms, detection type, and complexity of the identification process.
Compared with complementary metal–oxide semiconductor (CMOS), the resonant tunneling device (RTD) has better performances; it is the most promising candidate for next-generation integrated circuit devices. The universal logic gate is an important unit circuit because of its powerful logic function, but there are few function synthesis algorithms that can implement an n-variable logical function by RTD-based universal logic gates. In this paper, we propose a new concept, i.e., the truth value matrix. With it a novel disjunctive decomposition algorithm can be used to decompose an arbitrary n-variable logical function into three-variable subset functions. On this basis, a novel function synthesis algorithm is proposed, which can implement arbitrary n-variable logical functions by RTD-based universal threshold logic gates (UTLGs), RTD-based three-variable XOR gates (XOR3s), and RTD-based three-variable universal logic gate (ULG3s). When this proposed function synthesis algorithm is used to implement an n-variable logical function, if the function is a directly disjunctive decomposition one, the circuit structure will be very simple, and if the function is a non-directly disjunctive decomposition one, the circuit structure will be simpler than when using only UTLGs or ULG3s. The proposed function synthesis algorithm is straightforward to program, and with this algorithm it is convenient to implement an arbitrary n-variable logical function by RTD-based universal logic gates.
Cognitive radio is an effective technology to alleviate the spectrum resource scarcity problem by opportunistically allocating the spare spectrum to unauthorized users. However, a serious denial-of-service (DoS) attack, named the ‘primary user emulation attack (PUEA)’, exists in the network to deteriorate the system performance. In this paper, we propose a PUEA detection method that exploits the radio channel information to detect the PUEA in the cognitive radio network. In the proposed method, the uniqueness of the channel impulse response (CIR) between the secondary user (SU) and the signal source is used to determine whether the received signal is transmitted by the primary user (PU) or the primary user emulator (PUE). The closed-form expressions for the false-alarm probability and the detection probability of the proposed PUEA detection method are derived. In addition, a modified subspace-based blind channel estimation method is presented to estimate the CIR, in order for the proposed PUEA detection method to work in the scenario where the SU has no prior knowledge about the structure and content of the PU signal. Numerical results show that the proposed PUEA detection method performs well although the difference in channel characteristics between the PU and PUE is small.