Fuzzy clustering with learnable cluster-dependent kernels

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster-dependent Kernels (FLeCK), that learns the underlying cluster-dependent dissimilarity measure while seeking compact clusters. The learned dissimilarity is based on a Gaussian kernel function with cluster-dependent parameters. Each cluster’s parameter learned by FLeCK reflects the relative intra-cluster and inter-cluster characteristics. These parameters are learned by optimizing both the intra-cluster and the inter-cluster distances. This optimization is achieved iteratively by dynamically updating the partition and the local kernel. This makes the kernel learning task takes advantages of the available unlabeled data and reciprocally, the categorization task takes advantages of the learned local kernels. Another key advantage of FLeCK is that it is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. Using synthetic and real data sets, we show that FLeCK learns meaningful parameters and outperforms several other algorithms. In particular, we show that when data include clusters with various inter- and intra-cluster distances, learning cluster-dependent parameters is crucial in obtaining a good partition.     

Supervised Multi‐Rejector of Periodic Disturbances for Nonlinear Systems Using Decoupled State Multimodel

In this paper, a multi‐rejector of periodic disturbances is proposed for discrete‐time nonlinear systems represented by a decoupled state multimodel. We report a decoupled state multimodel repetitive‐predictive control based on a supervised algorithm to ensure reference trajectory tracking and periodic disturbances rejection. Partial predictors associated to the local controllers make the best choice of the most valid partial controller that meets the desired closed loop performances. The effectiveness of the supervised multi‐rejector is shown via a simulation example. The obtained results are satisfactory and show a good rejection of periodic disturbances and reference trajectory tracking.     

The Cost of Environmental Constraints in Traffic Networks: Assessing the Loss of Optimality

Increasing numbers of hard environmental constraints are being imposed in urban traffic networks by authorities in an attempt to mitigate pollution caused by traffic. However, it is not trivial for authorities to assess the cost of imposing such hard environmental constraints. This leads to difficulties when setting the constraining values as well as implementing effective control measures. For that reason, quantifying the cost of imposing hard environmental constraints for a certain network becomes crucial. This paper first indicates that for a given network, such cost is not only related to the attribution of environmental constraints but also related to the considered control measures. Next, we present an assessment criterion that quantifies the loss of optimality under the control measures considered by introducing the environmental constraints. The criterion can be acquired by solving a bi-level programming problem with/without environmental constraints. A simple case study shows its practicability as well as the differences between this framework and other frameworks integrating the environmental aspects. This proposed framework is widely applicable when assessing the interaction of traffic and its environmental aspects.     

Fuzzy fault tolerant predictive control for a diesel engine air path

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.     

基于灰关联混合蛙跳算法的雷达波形设计

提出了一种基于灰关联混合蛙跳算法的雷达波形设计方法,以混合蛙跳算法为主体,在局部更 新算子中引入遗传算法的遗传算子,并改进原始蛙跳算法的分组方法,丰富了种群的多样性,同时引入灰关联综合评价法则对适应度函数值加以关联度分析。文中以设计具有低自相关旁瓣和互相关特性的正交多相编码为例,将该算法用于雷达波形设计中。仿真结果表明使用本文算法产生 的波形具备较好的低自相关特性和互相关特性,表明了该算法是有效和可行的。     

Aggregate‐induced emission in light‐emitting liquid crystal display technology

Nowadays, liquid crystal displays (LCDs) with light‐emission are considered as energy‐efficient devices and are promising alternatives to conventional LCDs. To realize such possibility, strong fluorescent materials with a dichroic properties are required. Aggregate‐induced emission (AIE) is an unusual photophysical phenomenon shown by some luminogenic materials that will be highly emissive in their aggregate state. In this work, we studied the AIE effect of a luminescent liquid crystalline molecule TPE‐PPE in our LC system as a luminophore dopant. The result showed the excellent AIE effect that higher concentration of luminogen in the nematic LC host induced stronger luminescent intensity. Through exposure of a photoisomeriable alignment material sulfonic‐dye‐1, the photopatterning of a light‐emitting LC device was achieved with the use of the TPE‐PPE/nematic LC mixture.     

A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.     

User Preference for a Control-Based Reduced Processing Decision Support Interface

This study examined trainee crime-scene investigators' preference for, and accuracy in using, four different computer-based decision support interface designs, each of which incorporated a different reduced processing information acquisition strategy. The interfaces differed on the basis of the number of options that could be considered simultaneously and the level of control that could be exercised over the number and sequence in which feature values were accessed. Forty trainee investigators completed six decision scenarios in which they were asked to acquire information and formulate a decision by selecting one of three options. The study comprised two phases, the first of which involved familiarizing participants with each of the four interface designs and collecting performance and subjective data. The second phase involved trainees selecting one of the four interfaces to engage in a fifth and sixth decision scenario involving high or low levels of time-pressure. The results indicated that the “all options, full control” interface was the preferred option in the low time-pressure condition. Although the strategy remained the most frequently selected in the high time-pressure condition, this preference was not significant. It was concluded that the perceptions of difficulty and the degree of user control over information acquisition were more important than perceived efficiency in the selection of computer-based interface designs. The outcomes have implications for the design of decision support systems.     

Local nonlinear unknown input observer

This paper proposes a new nonlinear unknown input observer. The observer design approach utilizes the first order Taylor expansion. The observer gains are then obtained by a systematic method. In this paper, we added some improvements to this method. The developed approach also can enable observer design for a large class of differentiable nonlinear systems. The necessary and sufficient conditions for the existence of the observer are given. A numerical example is given to illustrate the attractiveness and the simplicity of the new design procedure.