SwePub - sökning: L773:0020 0255 OR L773:1872 62...

Numrering	Referens	Omslagsbild	Hitta
1.	Janzura, M., et al. (författare) Minimum entropy of error principle in estimation 1994 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 79:1-2, s. 123-144 Tidskriftsartikel (refereegranskat)abstract The principle of minimum error entropy estimation as found in the work of Weidemann and Stear is reformulated as a problem of finding optimum locations of probability densities in a given mixture such that the resulting (differential) entropy is minimized. New results concerning the entropy lower bound are derived. Continuity of the entropy and attaining the minimum entropy are proved in the case where the mixture is finite. Some other examples and situations, in particular that of symmetric unimodal densities, are studied in more detail
2.	Koski, Timo, et al. (författare) Some properties of generalized exponential entropies with applications to data compression 1992 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 62:1-2, s. 103-132 Tidskriftsartikel (refereegranskat)abstract I. Csiszár discussed generalized entropies in his lecture at the Sixth Prague Conference on Information Theory. The authors emphasize that Csiszár noted the link between certain lower bounds for the quantization error and Rényi's differential entropy of order $\alpha$. Another important reference is the paper by L. L. Campbell where the concept of an exponential entropy was introduced. The authors investigate "several consequences that are of interest in the theory of data (or signal) compression". They also "investigate especially the exponential families of distributions, in particular the Miller-Thomas (or generalized Gaussian) family of distributions". The paper is a detailed discussion of the aforementioned problems coupled with examples and details of the possible applications. Exponential entropy is calculated for the uniform distribution, the univariate Gaussian distribution, the Laplace distribution, the Miller-Thomas distribution, an infinite-dimensional Gaussian exponential family, the Gauss-Laplace mixture and the multivariate Gaussian distribution. The extent of a distribution is given for the shape parameter in the Miller-Thomas distribution. Campbell's representation for E$[\alpha, 1 ; f]$ and the connection between an entropy series and data compression are discussed. A lower bound for the entropy of a partition (as defined in the paper) is given. Examples and proofs are illustrated with outputs from Mathematica.
3.	Roy, Sisir, et al. (författare) Uncertainty Relations and Time-Frequency Distributions for Unsharp Observables 1996 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 89:3-4, s. 193-209 Tidskriftsartikel (refereegranskat)abstract This paper deals with a new framework in analyzing the formal mathematical correspondence between quantum mechanics and time-frequency representations of a signal. It is also shown that joint time-frequency distributions have a close link with Heisenberg uncertainty relations if the observables are taken as fuzzy entities. This result contradicts the arguments of Cohen [IEEE Proc. 77(7):941 (1989)] regarding the time-frequency distributions and the uncertainty relation. It is postulated that these mechanisms will be of crucial importance in highly fragmented computation structures, such as neural networks, as they may exhibit a strong mutual interaction between data and operator.
4.	Adewole, Kayode S., et al. (författare) Energy disaggregation risk resilience through microaggregation and discrete Fourier transform 2024 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 662 Tidskriftsartikel (refereegranskat)abstract Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.
5.	Altarabichi, Mohammed Ghaith, 1981-, et al. (författare) Rolling The Dice For Better Deep Learning Performance : A Study Of Randomness Techniques In Deep Neural Networks 2024 Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 667, s. 1-17 Tidskriftsartikel (refereegranskat)abstract This paper presents a comprehensive empirical investigation into the interactions between various randomness techniques in Deep Neural Networks (DNNs) and how they contribute to network performance. It is well-established that injecting randomness into the training process of DNNs, through various approaches at different stages, is often beneficial for reducing overfitting and improving generalization. However, the interactions between randomness techniques such as weight noise, dropout, and many others remain poorly understood. Consequently, it is challenging to determine which methods can be effectively combined to optimize DNN performance. To address this issue, we categorize the existing randomness techniques into four key types: data, model, optimization, and learning. We use this classification to identify gaps in the current coverage of potential mechanisms for the introduction of noise, leading to proposing two new techniques: adding noise to the loss function and random masking of the gradient updates.In our empirical study, we employ a Particle Swarm Optimizer (PSO) to explore the space of possible configurations to answer where and how much randomness should be injected to maximize DNN performance. We assess the impact of various types and levels of randomness for DNN architectures applied to standard computer vision benchmarks: MNIST, FASHION-MNIST, CIFAR10, and CIFAR100. Across more than 30\,000 evaluated configurations, we perform a detailed examination of the interactions between randomness techniques and their combined impact on DNN performance. Our findings reveal that randomness in data augmentation and in weight initialization are the main contributors to performance improvement. Additionally, correlation analysis demonstrates that different optimizers, such as Adam and Gradient Descent with Momentum, prefer distinct types of randomization during the training process. A GitHub repository with the complete implementation and generated dataset is available. © 2024 The Author(s)
6.	Aslam, Muhammad Shamrooz, et al. (författare) Robust stability analysis for class of Takagi-Sugeno (T-S) fuzzy with stochastic process for sustainable hypersonic vehicles 2023 Ingår i: Information Sciences. - Amsterdam : Elsevier. - 0020-0255 .- 1872-6291. ; 641 Tidskriftsartikel (refereegranskat)abstract Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics. © 2023 Elsevier Inc.
7.	Aslani, Mohammad, et al. (författare) Efficient and decision boundary aware instance selection for support vector machines 2021 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 577, s. 579-598 Tidskriftsartikel (refereegranskat)abstract Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH.
8.	Beikmohammadi, Ali, 1995-, et al. (författare) Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge 2024 Ingår i: Information Sciences. - 0020-0255 .- 1872-6291. ; 661 Tidskriftsartikel (refereegranskat)abstract Despite the huge success of reinforcement learning (RL) in solving many difficult problems, its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking advantage of prior knowledge, intentionally or unintentionally, has usually been avoided, so that, training an agent from scratch is common. This not only causes sample inefficiency but also endangers safety –especially during exploration. In this paper, we help the agent learn from the environment by using the pre-existing (but not necessarily exact or complete) solution for a task. Our proposed method can be integrated with any RL algorithm developed based on policy gradient and actor-critic methods. The results on five tasks with different difficulty levels by using two well-known actor-critic-based methods as the backbone of our proposed method (SAC and TD3) show our success in greatly improving sample efficiency and final performance. We have gained these results alongside robustness to noisy environments at the cost of just a slight computational overhead, which is negligible.
9.	Corizzo, Roberto, et al. (författare) Multi-aspect renewable energy forecasting 2021 Ingår i: Information Sciences. - Netherlands : Elsevier. - 0020-0255 .- 1872-6291. ; 546, s. 701-722 Tidskriftsartikel (refereegranskat)abstract The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms. © 2020 Elsevier Inc.
10.	Davidsson, Paul, et al. (författare) Distributed Monitoring and Control of Office Buildings by Embedded Agents 2005 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 171:4, s. 293-307 Tidskriftsartikel (refereegranskat)abstract We describe a decentralized system consisting of a collection of software agents that monitor and control an office building. It uses the existing power lines for communication between the agents and the electrical devices of the building, such as sensors and actuators for lights and heating. The objectives are both energy saving and increasing customer satisfaction through value added services. Results of qualitative simulations and quantitative analysis based on thermodynamical modeling of an office building and its staff using four different approaches for controlling the building indicate that significant energy savings can result from using the agent-based approach. The evaluation also shows that customer satisfaction can be increased in most situations. The approach here presented makes it possible to control the trade-off between energy saving and customer satisfaction (and actually increase both, in comparison with current approaches).
11.	Deng, Jifei, et al. (författare) Offline reinforcement learning for industrial process control : A case study from steel industry 2023 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 632, s. 221-231 Tidskriftsartikel (refereegranskat)abstract Flatness is a crucial indicator of strip quality that presents a challenge in regulation due to the high-speed process and the nonlinear relationship between flatness and process parameters. Conventional methods for controlling flatness are based on the first principles, empirical models, and predesigned rules, which are less adaptable to changing rolling conditions. To address this limitation, this paper proposed an offline reinforcement learning (RL) based data-driven method for flatness control. Based on the data collected from a factory, the offline RL method can learn the process dynamics from data to generate a control policy. Unlike online RL methods, the proposed method does not require a simulator for training, the policy can be potentially safer and more accurate since a simulator involves simplifications that can introduce bias. To obtain a steady performance, the proposed method incorporated ensemble Q-functions into policy evaluation to address uncertainty estimation. To address distributional shifts, based on Q-values from ensemble Q-functions, behavior cloning was added to policy improvement. Simulation and comparison results showed that the proposed method outperformed the state-of-the-art offline RL methods and achieved the best performance in producing strips with lower flatness.
12.	Doherty, Patrick, et al. (författare) A landscape and implementation framework for probabilistic rough sets using PROBLOG 2022 Ingår i: Information Sciences. - : Elsevier Science Inc. - 0020-0255 .- 1872-6291. ; 593, s. 546-576 Tidskriftsartikel (refereegranskat)abstract Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using PROBLOG, a popular and widely used probabilistic logic programming language based on PROBLOG. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic PROBLOG relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run PROBLOG code is included in the Appendix for all examples considered.
13.	Du, X., et al. (författare) Efficient methods with polynomial complexity to determine the reversibility of general 1D linear cellular automata over Zp 2022 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 594, s. 163-176 Tidskriftsartikel (refereegranskat)abstract The property of reversibility is quite meaningful for the classic theoreticabl computer science model, cellular automata. This paper focuses on the reversibility of general one-dimensional (1D) linear cellular automata (LCA), under null boundary conditions over the finite field Zp. Although the existing approaches have split the reversibility challenge into two sub-problems: calculate the period of reversibility first, then verify the reversibility in a period, they are still exponential in the size of the CA's neighborhood. In this paper, we use two efficient algorithms with polynomial complexity to tackle these two challenges, making it possible to solve large-scale reversible LCA, which substantially enlarge its applicability. Finally, we provide an interesting perspective to inversely generate a 1D LCA from a given period of reversibility.
14.	Elahi, Haroon, et al. (författare) Forward-porting and its limitations in fuzzer evaluation 2024 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 662 Tidskriftsartikel (refereegranskat)abstract Forward-porting reintroduces previously detected and patched software bugs from older versions into later ones to create benchmarking workloads for fuzzing. These benchmarks gauge a fuzzer's performance by testing its ability to detect or trigger these bugs during a fuzzing campaign. In this study, we evaluate the reliability of forward porting in establishing dependable fuzzing benchmarks and their suitability for fair and accurate fuzzer evaluation. We utilize online resources, forward porting, fuzzing experiments, and triaging to scrutinize the workloads of a state-of-the-art fuzzing benchmark. We uncover seven factors, including software architecture changes, misconfigurations, supply chain issues, and developer errors, all of which compromise the success of forward porting. We determine that the ‘ground truth’ established through forward porting is only occasionally ‘true’ due to unaccounted-for underlying bugs in all examined software applications undergoing this process. These findings question the reliability of forward porting in generating dependable fuzzing benchmarks. Furthermore, our experimental results suggest that relying on forward porting-based ground truth and verification metrics could lead to misleading evaluations of fuzzer performance. Ultimately, we propose insights into the development of fuzzing benchmarks to ensure more dependable assessments of fuzzers.
15.	Fan, Qingfeng, et al. (författare) Game Balanced Multi-factor Multicast Routing in Sensor Grid Networks 2016 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 367-368, s. 550-572 Tidskriftsartikel (refereegranskat)abstract In increasingly important sensor grid networks, multicast routing is widely used in date aggregation and distributed query processing. It requires multicast trees for efficient data transmissions. However, sensor nodes in such networks typically have limited resources and computing power. Efforts have been made to consider the space, energy and data factors separately to optimize the network performance. Considering these factors simultaneously, this paper presents a game balance based multi-factor multicast routing approach for sensor grid networks. It integrates the three factors into a unified model through a linear combination. The model is standardized and then solved theoretically by using the concept of game balance from game theory. The solution gives Nash equilibrium, implying a well balanced result for all the three factors. The theoretic results are implemented in algorithms for cluster formation, cluster core selection, cluster tree construction, and multicast routing. Extensive simulation experiments show that the presented approach gives mostly better overall performance than benchmark methods.
16.	Jenkins, Samantha, 1967-, et al. (författare) Software architecture graphs as complex networks : a novel partitioning scheme to measure stability and evolution 2007 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 177:12, s. 2587-2601 Tidskriftsartikel (refereegranskat)abstract The stability and evolution of the structure of consecutive versions of a series of software architecture graphs are analysed using the theory of complex networks. Brief comparisons are drawn between the scale-free behaviour and second order phase transitions. On this basis a software design metric Icc is proposed. This software metric is used to quantify the evolution of the stability vs. maintainability of the software through various releases. It is demonstrated that the classes in the software graph are acquiring more out-going calls than incoming calls as the software ages. Three examples of software applications where maintainability and continuous refactoring are an inherent part of their development process are presented, in addition to a Sun Java2 framework where growth and backward compatibility are the more important factors for the development. Further to this a projected future evolution of the software structure and maintainability is calculated. Suggestions for future applications to software engineering and the natural sciences are briefly presented.
17.	Jin, Junchen, et al. (författare) A non-parametric Bayesian framework for traffic-state estimation at signalized intersections 2019 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 498, s. 21-40 Tidskriftsartikel (refereegranskat)abstract An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role in real-time operations to facilitate efficient traffic management. This paper presents a generalized modeling framework for estimating traffic states at signalized intersections. The framework is non-parametric and data-driven, without any requirement on explicit modeling of traffic flow. The Bayesian filter (BF) approach is the core of the framework and introduces a recursive state estimation process. The required transition and measurement models of the BFs are trained using Gaussian process (GP) regression models with respect to a historical dataset. In addition to the detailed derivation of the integration of BFs and GP regression models, an algorithm based on the extended Kalman filter is presented for real-time traffic estimation. The effectiveness of the proposed framework is demonstrated through several numerical experiments using data generated in microscopic traffic simulations. Both fixed-location data (i.e., loop detector) and mobile data (i.e., connected vehicle) are examined with the framework. As a result, the method shows good performance under the different traffic conditions in the experiment. In particular, the approach is suitable for short-term estimation, a challenging task in traffic control and operations.
18.	Khrennikov, Andrei, 1958- (författare) Nonlocality as well as rejection of realism are only sufficient (but non-necessary!) conditions for violation of Bell's inequality 2009 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 179:5, s. 492-504 Tidskriftsartikel (refereegranskat)abstract In this review we remind the viewpoint that violation of Bell’s inequality might be interpreted not only as an evidence of the alternative – either nonlocality or “death of reality” (under the assumption the quantum mechanics is incomplete). Violation of Bell’s type inequalities is a well known sufficient condition of probabilistic incompatibility of random variables – impossibility to realize them on a single probability space. Thus, in fact, we should take into account an additional interpretation of violation of Bell’s inequality – a few pairs of random variables (two-dimensional vector variables) involved in the EPR–Bohm experiment are incompatible. They could not be realized on a single Kolmogorov probability space. Thus, one can choose between: (a) completeness of quantum mechanics; (b) nonlocality; (c) “ death of reality”; (d) non-Kolmogorovness. In any event, violation of Bell’s inequality has a variety of possible interpretations. Hence, it could not be used to obtain the definite conclusion on the relation between quantum and classical models.
19.	Li, Jun, et al. (författare) An efficient and reliable approach for quality-of-service-aware service composition 2014 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 269, s. 238-254 Tidskriftsartikel (refereegranskat)abstract With the rapidly increasing number of independently developed Web services that provide similar functionalities with varied quality of service (QoS), service composition is considered as a problem in the selection of component services that are in accordance with users' QoS requirements; a practice known as the QoS-aware service composition problem. However, current solutions are unsuitable for most real-time decision-making service composition applications required to obtain a relatively optimal result within a reasonable amount of time. These services are also unreliable (or even risky) given the open service-oriented environment. In this paper, we address these problems and propose a novel heuristic algorithm for an efficient and reliable selection of trustworthy services in a service composition. The proposed algorithm consists of three steps. First, a trust-based selection method is used to filter untrustworthy component services. Second, convex hulls are constructed to reduce the search space in the process of service composition. Finally, a heuristic global optimization approach is used to obtain the near-optimal solution. The results demonstrate that our approach obtains a close-to-optimal and reliable solution within a reasonable computation time.
20.	Li, Mianjie, et al. (författare) A dual-embedded tamper detection framework based on block truncation coding for intelligent multimedia systems 2023 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 649 Tidskriftsartikel (refereegranskat)abstract The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia ser-vices, such as tampering, hijacking, and adversarial attacks. Therefore, this paper proposes a dual -embedding framework based on block truncation coding to improve the security of intelligent multimedia systems. First, the signal is decomposed in frequency domain by using approximate translation invariance to obtain multi-layer frequency-domain parameters; then, this paper hides the encrypted data in two layers of low-frequency coefficients through fragile and robust embedding algorithms, respectively. In addition, in order to further improve the security per-formance, this paper adopts the method of block truncation coding to encrypt the embedded data. On the basis of performance analysis, the superiority of this method is illustrated by comparing with the existing methods.
21.	López, D., et al. (författare) BELIEF : A distance-based redundancy-proof feature selection method for Big Data 2021 Ingår i: Information Sciences. - : Elsevier Inc.. - 0020-0255 .- 1872-6291. ; 558, s. 124-139 Tidskriftsartikel (refereegranskat)abstract With the advent of Big Data era, data reduction methods are in highly demand given their ability to simplify huge data, and ease complex learning processes. Concretely, algorithms able to select relevant dimensions from a set of millions are of huge importance. Although effective, these techniques also suffer from the “scalability” curse when they are brought into tackle large-scale problems. In this paper, we propose a distributed feature weighting algorithm which precisely estimates feature importance in large datasets using the well-know algorithm RELIEF in small problems. Our solution, called BELIEF, incorporates a novel redundancy elimination measure that generates similar schemes to those based on entropy, but at a much lower time cost. Furthermore, BELIEF provides a smooth scale-up when more instances are required to increase precision in estimations. Empirical tests performed on our method illustrate the estimation ability of BELIEF in manifold huge sets – both in number of features and instances, as well as its reduced runtime cost as compared to other state-of-the-art methods.
22.	Marinakis, Y., et al. (författare) A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows 2019 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 481, s. 311-329 Tidskriftsartikel (refereegranskat)abstract In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature.
23.	Ning, Xin, et al. (författare) ICGNet : An intensity-controllable generation network based on covering learning for face attribute synthesis 2024 Ingår i: Information Sciences. - New York : Elsevier. - 0020-0255 .- 1872-6291. ; 660 Tidskriftsartikel (refereegranskat)abstract Face-attribute synthesis is a typical application of neural network technology. However, most current methods suffer from the problem of uncontrollable attribute intensity. In this study, we proposed a novel intensity-controllable generation network (ICGNet) based on covering learning for face attribute synthesis. Specifically, it includes an encoder module based on the principle of homology continuity between homologous samples to map different facial images onto the face feature space, which constructs sufficient and effective representation vectors by extracting the input information from different condition spaces. It then models the relationships between attribute instances and representational vectors in space to ensure accurate synthesis of the target attribute and complete preservation of the irrelevant region. Finally, the progressive changes in the facial attributes by applying different intensity constraints to the representation vectors. ICGNet achieves intensity-controllable face editing compared to other methods by extracting sufficient and effective representation features, exploring and transferring attribute relationships, and maintaining identity information. The source code is available at https://github.com/kllaodong/-ICGNet.•We designed a new encoder module to map face images of different condition spaces into face feature space to obtain sufficient and effective face feature representation.•Based on feature extraction, we proposed a novel Intensity-Controllable Generation Network (ICGNet), which can realize face attribute synthesis with continuous intensity control while maintaining identity and semantic information.•The quantitative and qualitative results showed that the performance of ICGNet is superior to current advanced models.© 2024 Elsevier Inc.
24.	Ontkovičová, Zuzana, et al. (författare) Computation of Choquet integrals: Analytical approach for continuous functions 2024 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 679 Tidskriftsartikel (refereegranskat)abstract In the continuous case, analytical computations of the Choquet integral are limited, despite being commonly used in various applications. One can either use the definition, which is computationally demanding and impractical, or apply already existing formulas restricted only to monotone nonnegative functions on a real interval starting at zero. This article aims to present more convenient computational formulas for continuous functions without imposing restrictions on their monotonicity given any real interval. First, a more general approach to monotone functions is provided for both positive and negative functions. Then, reordering techniques are introduced to compute the Choquet integral of an arbitrary continuous function, and with these, a monotone equivalent to every function can be constructed. This equivalent function preserves the final Choquet integral value, implying that only formulas for monotone functions are required. In addition to general fuzzy measures, the article assumes particular cases of distorted Lebesgue measures and distorted probabilities as the most commonly used fuzzy measures.
25.	Pan, Yuchen, et al. (författare) An online-to-offline service recommendation method based on two-layer knowledge networks 2023 Ingår i: Information Sciences. - 0020-0255 .- 1872-6291. ; 648 Tidskriftsartikel (refereegranskat)abstract This paper introduces a novel method aimed at enhancing onlinetooffline (O2O) services recommendations by utilizing twolayer knowledge networks. The primary objective of this method is to assist consumers in efficiently navigating the myriad of options available when choosing O2O services. Using co-occurrence relationships, we construct a two-layer knowledge network system, comprising a service knowledge network based on service usage information as the first layer and a consumer knowledge network, built on co-used behaviors as the second layer. The former is established upon service use data, while the latter is founded on co-used behaviors among consumers. The features and information of these two knowledge networks can complement each other to produce precise and effective recommendations. Empirical findings gained from our experiments demonstrate that: (1) the proposed recommendation method outperforms widely-used and state-of-the-art recommendation methods; (2) both the service knowledge network and consumer knowledge network play an equally significant role in O2O service recommendations; (3) the location of O2O services is an essential factor in consumers' choices for services. Notably, this research also identifies the optimal parameter settings for the proposed recommendation method.
26.	Pan, Yuchen, et al. (författare) User activity measurement in rating-based online-to-offline (O2O) service recommendation 2019 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 479, s. 180-196 Tidskriftsartikel (refereegranskat)abstract The increasing popularity of O2O service make more and more people begin seeking and booking services online. After that, they experience the services in brick-and-mortar stores. This new business model has marketing potential and offer various opportunities to different industries. Consequently, various O2O services starting to appear, which results in difficult service selections for customers. Therefore, in this paper, we proposed a novel rating-based O2O service recommendation model considering user activity. In this method, the traditional similarity estimations are substituted by user activity which can better reflect the differentiations of customers' behavioral characteristics. Therefore, recommendations are more accurate. The experimental results show that proposed method outperforms rating-based methods, including widely used collaborative filtering methods and state-of-the-art matrix methods. In addition, we find the optimal parameter values of our model, and explore the influence of Top-k on rating-based recommendation.
27.	Qu, Zhiguo, et al. (författare) Quantum detectable Byzantine agreement for distributed data trust management in blockchain 2023 Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 637 Tidskriftsartikel (refereegranskat)abstract No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors
28.	Shamrooz Aslam, Muhammad, et al. (författare) A delayed Takagi–Sugeno fuzzy control approach with uncertain measurements using an extended sliding mode observer 2023 Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 643 Tidskriftsartikel (refereegranskat)abstract In this study, a sliding mode observer (SMO) is implemented on a T–S fuzzy system with multiple time–varying delays over continuous time. Because state data may not be fully available in practice, state observers are used to estimate state information. A system based on observers is implemented with non–parallel distribution compensation (N-PDC). Moreover, the concept of dissipative control provides a framework for analyzing the performance of H∞, L2−L∞, and dissipativeness. In order to design two sliding surfaces using the SMO gain matrix, first two integral–type sliding surfaces must be constructed. Then, we define a few additional parameters using fuzzy Lyapunov stability and SMO theory, resulting in asymptotically stable closed–loop performances. On the basis of the new error system, convex optimization is used to generate the sliding mode controller and the gained weight matrices. Following is an example of the power system (ship electric propulsion) to demonstrate the potential scheme. © 2023 Elsevier Inc.
29.	Wang, Chunpeng, et al. (författare) Quaternion polar harmonic Fourier moments for color images 2018 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 450, s. 141-156 Tidskriftsartikel (refereegranskat)abstract This paper proposes quaternion polar harmonic Fourier moments (QPHFM) for color image processing and analyzes the properties of QPHFM. After extending Chebyshev–Fourier moments (CHFM) to quaternion Chebyshev-Fourier moments (QCHFM), comparison experiments, including image reconstruction and color image object recognition, on the performance of QPHFM and quaternion Zernike moments (QZM), quaternion pseudo-Zernike moments (QPZM), quaternion orthogonal Fourier-Mellin moments (QOFMM), QCHFM, and quaternion radial harmonic Fourier moments (QRHFM) are carried out. Experimental results show QPHFM can achieve an ideal performance in image reconstruction and invariant object recognition in noise-free and noisy conditions. In addition, this paper discusses the importance of phase information of quaternion orthogonal moments in image reconstruction.
30.	Wang, Leyao, et al. (författare) A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites 2023 Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 630, s. 40-52 Tidskriftsartikel (refereegranskat)abstract N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool. © 2023 Elsevier Inc. All rights reserved.
31.	Wang, Zifan, et al. (författare) Distributed dynamic event-triggered communication and control for multi-agent consensus : A hybrid system approach 2022 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 618, s. 191-208 Tidskriftsartikel (refereegranskat)abstract This paper investigates the output-based event-triggered communication and control for linear multi-agent consensus under a directed graph based on a co-design method. The communication among the agents, as well as the controller updates, are determined by some new event-triggering mechanisms, in order to reduce the use of the network resources. To simultaneously guarantee the significant properties including the asymptotic consensus, and strong Zeno-freeness (strictly positive inter-event times), a novel dis-tributed dynamic event-triggered protocol is proposed. Unlike the most-existing emulation-based approaches in which the control gain is previously decided, a systematic co-design procedure is proposed to design the controller gain, the observer gain, and the event-triggering mechanisms altogether in terms of solving a linear matrix inequality opti-mization problem. Based on the resulting hybrid system framework, a hybrid model is established for the distributed closed-loop system and the asymptotic consensus is achieved. Finally, a numerical example is presented to verify our systematic design methodology.
32.	Wang, Zhenwu, et al. (författare) PML-ED : A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation 2024 Ingår i: Information Sciences. - 0020-0255 .- 1872-6291. ; 661 Tidskriftsartikel (refereegranskat)abstract Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the Encoder-Decoder Framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal Encoder-Decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.
33.	Wei, Wei, et al. (författare) Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network 2017 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 408, s. 100-114 Tidskriftsartikel (refereegranskat)abstract Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.
34.	Wu, Dan, 1971-, et al. (författare) A method for creating and using a context knowledge base for ontology integration 2014 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. Tidskriftsartikel (refereegranskat)abstract Ontology integrations are applied for generating new ontologies used for providing advanced services. Although many algorithms and systems for ontology integration have been proposed, it is still very difficult to achieve semantic and pragmatic ontology matching and integration. To tackle the problems, a method of building and using a context knowledge base is needed to get the context of ontologies used for matching and integration. In the method, a context knowledge base with context rules is developed upon an ontology repository to improve the ontology integration. In the context knowledge base, context rules use Ontology Metadata Vocabulary to describe contexts. The stored ontologies that in the repository satisfy a particular context, are extracted and integrated automatically to form contextual information. In the contextual information, both the consistent ontological definitions and the different perspectives from various ontologies of string-identical entities are used. For new ontology integrations, context rules are searched and triggered according to the context of the ontology integration at hand. The contextual information of the rules is aggregated for improving the new ontology integration. Meta-rules are also automatically built to be able to apply the context rules in a hierarchical relation. Experiments of integrations show that the context knowledge base provides extra contextual information for ontology integration. Moreover, when comparing an integration using the context knowledge base to an ontology integration without the context knowledge base, the results of the contextual ontology integrations are improved. At the same time, it is observed that the quantity and the quality of the stored ontologies, in a repository, determine the quality of the context knowledge base. The better quality and quantity of the context knowledge base, the higher degree of improvement it infers to the contextual ontology integrations.
35.	Xia, Zhihua, et al. (författare) EPCBIR : An efficient and privacy-preserving content-based image retrieval scheme in cloud computing 2017 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 387, s. 195-204 Tidskriftsartikel (refereegranskat)abstract The content-based image retrieval (CBIR) has been widely studied along with the increasing importance of images in our daily life. Compared with the text documents, images consume much more storage and thus are very suitable to be stored on the cloud servers. The outsourcing of CBIR to the cloud servers can be a very typical service in cloud computing. For the privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before being outsourced, which will cause the CBIR technologies in plaintext domain unusable. In this paper, we propose a scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server. Firstly, the feature vectors are extracted to represent the corresponding images. Then, the pre-filter tables are constructed with the locality-sensitive hashing to increase the search efficiency. Next, the feature vectors are protected by the secure k-nearest neighbor (kNN) algorithm. The security analysis and experiments show the security and efficiency of the proposed scheme.
36.	Yan, Su-Rong, et al. (författare) A graph-based comprehensive reputation model : exploiting the social context of opinions to enhance trust in social commerce 2015 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 318, s. 51-72 Tidskriftsartikel (refereegranskat)abstract Social commerce is a promising new paradigm of e-commerce. Given the open and dynamic nature of social media infrastructure, the governance structures of social commerce are usually realized through reputation mechanisms. However, the existing approaches to the prediction of trust in future interactions are based on personal observations and/or publicly shared information in social commerce application. As a result, the indications are unreliable and biased because of limited first-hand information and stake-holder manipulation for personal strategic interests. Methods that extract trust values from social links among users can improve the performance of reputation mechanisms. Nonetheless, these links may not always be available and are typically sparse in social commerce, especially for new users. Thus, this study proposes a new graph-based comprehensive reputation model to build trust by fully exploiting the social context of opinions based on the activities and relationship networks of opinion contributors. The proposed model incorporates the behavioral activities and social relationship reputations of users to combat the scarcity of first-hand information and identifies a set of critical trust factors to mitigate the subjectivity of opinions and the dynamics of behaviors. Furthermore, we enhance the model by developing a novel deception filtering approach to discard "bad-mouthing" opinions and by exploiting a personalized direct distrust (risk) metric to identify malicious providers. Experimental results show that the proposed reputation model can outperform other trust and reputation models in most cases. (C) 2014 Elsevier Inc. All rights reserved.
37.	Yang, Junjun, et al. (författare) A model-based deep reinforcement learning approach to the nonblocking coordination of modular supervisors of discrete event systems 2023 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 630, s. 305-321 Tidskriftsartikel (refereegranskat)abstract Modular supervisory control may lead to conflicts among the modular supervisors for large-scale discrete event systems. The existing methods for ensuring nonblocking control of modular supervisors either exploit favorable structures in the system model to guarantee the nonblocking property of modular supervisors or employ hierarchical model abstraction methods for reducing the computational complexity of designing a nonblocking coordinator. The nonblocking modular control problem is, in general, NP-hard. This study integrates supervisory control theory and a model-based deep reinforcement learning method to synthesize a nonblocking coordinator for the modular supervisors. The deep reinforcement learning method significantly reduces the computational complexity by avoiding the computation of synchronization of multiple modular supervisors and the plant models. The supervisory control function is approximated by the deep neural network instead of a large-sized finite automaton. Furthermore, the proposed model-based deep reinforcement learning method is more efficient than the standard deep Q network algorithm.
38.	Yi, J.-H., et al. (författare) Behavior of crossover operators in NSGA-III for large-scale optimization problems 2020 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 509, s. 470-487 Tidskriftsartikel (refereegranskat)abstract Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
39.	Zamli, Kamal Z., et al. (författare) An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation 2017 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 399, s. 121-153 Tidskriftsartikel (refereegranskat)abstract Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance. (C) 2017 Elsevier Inc. All rights reserved.
40.	Zeyu, He, et al. (författare) Causal embedding of user interest and conformity for long-tail session-based recommendations 2023 Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 644 Tidskriftsartikel (refereegranskat)abstract Session-based recommendation is misleading by popularity bias and always favors short-head items with more popularity. This paper studies a new causal-based framework CauTailReS to increase the diversity of session recommendations. We first propose a new causal graph and then use the do-calculus in order to understand how popularity influences the process of making recommendations from the user's point of view. Popularity only misleads users temporarily, rather than in a long term and globally. Second, we believe that user clicks on popular products demonstrate their high quality and reputation. CauTailReS only eliminates ‘bad’ biases and retains ‘good’ effects through interest and consistent causal embedding mechanisms. To determine how similar various users are on various target items, CauTailReS also employs a re-ranking technique known as ‘conformity-aware re-ranking’. To discover interactions based on what actual users want, CauTailReS also employs counterfactual reasoning. Extensive comparative experiments on four real world datasets have shown CauTailReS can well capture the true interests and consistency of users. As compared to the current state-of-the-art, CauTailReS enhances long-tail performance (APLT is increased by 8.14%) and recommendation accuracy (MRR is increased by 2.75%). This proves that introducing causal embeddings helps to reasonably enhance the diversity of recommendations. © 2023 Elsevier Inc.
41.	Zhang, Qingke, et al. (författare) Vector coevolving particle swarm optimization algorithm 2017 Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 394-395, s. 273-298 Tidskriftsartikel (refereegranskat)abstract In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.
42.	Hagander, Per (författare) Numerical Solution of $A^TS+SA+Q=0$ 1972 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255. ; 4:1, s. 35-50 Tidskriftsartikel (refereegranskat)abstract A survey of techniques to solve ATS + SA + Q = 0 is presented, and nine algorithms are coded and tested on a batch of examples. Which algorithm to be recommended depends mainly on the order of the system.
43.	Åström, Karl Johan (författare) On the Choice of Sampling Rates in Parametric Identification of Time Series 1969 Ingår i: Information Sciences. - 0020-0255. ; 1:3, s. 273-278 Tidskriftsartikel (refereegranskat)abstract Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. In parametric it appears at first sight that no such limitations are present. In this note we will obtain insight into this paradox by analyzing a simple Gauss-Markov process. We assume that a time series analysis is performed based on N samples of the series at equal spacing h. The result shows that there is an optimal choice of h and that the variance increases rapidly when h increases from the optimal value. The analysis of a time series of fixed length T with different number of samplings is also discussed.
44.	Allahviranloo, T, et al. (författare) Solving fuzzy differential equations by differential transformation method 2009 Ingår i: INFORMATION SCIENCES. - 0020-0255. ; 179:7, s. 956-966 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
45.	Allahviranloo, T, et al. (författare) Toward the existence and uniqueness of solutions of second-order fuzzy differential equations 2009 Ingår i: INFORMATION SCIENCES. - 0020-0255. ; 179:8, s. 1207-1215 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
46.	Arshad, Hamed, 1987, et al. (författare) Semantic Attribute-Based Encryption: A framework for combining ABE schemes with semantic technologies 2022 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255. ; 616, s. 558-576 Tidskriftsartikel (refereegranskat)abstract Attribute-Based Encryption (ABE) is a cryptographic solution to protect resources in a fine-grained manner based on a set of public attributes. This is similar to attribute-based access control schemes in the sense that both rely on public attributes and access control policies to grant access to resources. However, ABE schemes do not consider the semantics of attributes provided by users or required by access structures. Such semantics not only improve the functionality by making proper access decisions but also enable cross-domain interoperability by making users from one domain able to access and use resources of other domains. This paper proposes a Semantic ABE (SABE) framework by augmenting a classical Ciphertext-Policy ABE (CP-ABE) scheme with semantic technologies using a generic procedure by which any CP-ABE scheme can be extended to an SABE. The proposed SABE framework is implemented in Java and the source code is publicly available. The experiment results confirm that the performance of the proposed framework is promising.
47.	Ge, Chenjie, 1991, et al. (författare) A spiking neural network model for obstacle avoidance in simulated prosthetic vision 2017 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255. ; 399:August 2017, s. 30-42 Tidskriftsartikel (refereegranskat)abstract Limited by visual percepts elicited by existing visual prosthesis, it’s necessary to enhance its functionality to fulfill some challenging tasks for the blind such as obstacle avoidance. This paper argues that spiking neural networks (SNN) are effective techniques for object recognition and introduces for the first time a SNN model for obstacle recognition to as- sist blind people wearing prosthetic vision devices by modelling and classifying spatio- temporal (ST) video data. The proposed methodology is based on a novel spiking neural network architecture, called NeuCube as a general framework for video data modelling in simulated prosthetic vision. As an integrated environment including spiking trains en- coding, input variable mapping, unsupervised reservoir training and supervised classifier training, the NeuCube consists of a spiking neural network reservoir (SNNr) and a dy- namic evolving spiking neural network classifier (deSNN). First, input data is captured by visual prosthesis, then ST feature extraction is utilized in the low-resolution prosthetic vi- sion generated by prostheses. Finally such ST features are fed to the NeuCube to output classification result of obstacle analysis for an early warning system to be activated. Ex- periments on collected video data and comparison with other computational intelligence methods indicate promising results. This makes it possible to directly utilize available neu- romorphic hardware chips, embedded in visual prostheses, to enhance significantly their functionality. The proposed NeuCube-based obstacle avoidance methodology provides use- ful guidance to the blind, thus offering a significant improvement of current prostheses and potentially benefiting future prosthesis wearers.
48.	Huang, Na, et al. (författare) Distributed and adaptive triggering control for networked agents with linear dynamics 2020 Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255. ; 517, s. 297-314 Tidskriftsartikel (refereegranskat)abstract This paper proposes distributed event-triggered schemes for achieving state consensus for multi-agent linear systems. For each agent modeled by a linear control system in Rn, a positive signal is embedded in its event function, with the aim of guaranteeing an asymptotic convergence to state consensus for networked linear systems interacted in an undirected and connected graph, and with Zeno triggering excluded for all the agents. The proposed distributed event-based consensus algorithm allows each agent to update its own control at its own triggering times instead of using continuous updates, which thereby avoids complicated computation steps involving data fusion and matrix exponential calculations as used in several event-based control schemes reported in the literature. We further propose a totally distributed and adaptive event-based algorithm, in the sense that each agent utilizes only local measurements with respect to its neighboring agents in its event detection and control update. In this framework, the proposed algorithm is independent of any global network information such as Laplacian matrix eigenvalues associated with the underlying interaction graph. A positive L1 signal function is included in the adaptive event-based algorithm to guarantee asymptotic consensus convergence and Zeno-free triggering for all the agents. Simulations are provided to validate the performance and superiority of the developed event-based consensus strategies.

Skapa referenser, mejla, bekava och länka

Länka till träfflistan

Träfflista för sökning "L773:0020 0255 OR L773:1872 6291 "

Avgränsa träffmängd

År