Lane Detection With Moving Vehicles in the Traffic Scenes

A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief. First, lane marks are extracted based on color information. The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy. Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks. The mechanism effectively eliminates the influence of passing vehicles when performing lane detection. Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane. The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic. Experimental results show that the proposed method works well on marked roads in various lighting conditions     

Rare Gain-of-Function KCND3 Variant Associated with Cerebellar Ataxia,Parkinsonism, Cognitive Dysfunction,and Brain Iron Accumulation

Loss-of-function mutations in the K_V4.3 channel-encoding KCND3 gene are linked to neurodegenerative cerebellar ataxia. Patients suffering from neurodegeneration associated with iron deposition may also present with cerebellar ataxia. The mechanism underlying brain iron accumulation remains unclear. Here, we aim to ascertain the potential pathogenic role of KCND3 variant in iron accumulation-related cerebellar ataxia. We presented a patient with slowly progressive cerebellar ataxia, parkinsonism, cognitive impairment, and iron accumulation in the basal ganglia and the cerebellum. Whole exome sequencing analyses identified in the patient a heterozygous KCND3 c.1256G>A (p.R419H) variant predicted to be disease-causing by multiple bioinformatic analyses. In vitro biochemical and immunofluorescence examinations revealed that, compared to the human K_V4.3 wild-type channel, the p.R419H variant exhibited normal protein abundance and subcellular localization pattern. Electrophysiological investigation, however, demonstrated that the K_V4.3 p.R419H variant was associated with a dominant increase in potassium current amplitudes, as well as notable changes in voltage-dependent gating properties leading to enhanced potassium window current. These observations indicate that, in direct contrast with the loss-of-function KCND3 mutations previously reported in cerebellar ataxia patients, we identified a rare gain-of-function KCND3 variant that may expand the clinical and molecular spectra of neurodegenerative cerebellar disorders associated with brain iron accumulation.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

Surface treatment,reflectance, and age effects on electronic‐paper reading performance

Abstract— This study is intended to explore the legibility and visual fatigue of different age users under various surface treatments and reflectance of electronic paper. Through the method of character‐search task, the results indicated that compared with single types of treatment [anti‐reflection (AR) 0.8%, anti‐glare, 43% haze), the compound treatment of anti‐reflection and anti‐glare (ARC) exhibited the same legibility, and it showed superior properties to effectively reduce visual fatigue. Hence, it is suggested that electronic‐paper manufacturers should choose the compound surface treatment for better visual performance. On the other hand, the findings also validated that enhancing the reflectance of electronic paper to the same level as regular paper (about 80%) is worthy to be practically implemented. Based on the results of this study, electronic‐paper manufacturers can take useful information to fulfill ergonomic requirements on product design.     

Exploiting knowledge ontology and software agents for PPI network analysis

One major goal of functional genomics has been to identify and analyze molecular interactions in a cellular context to better understand the underlying design principles and mechanisms. To investigate into a PPI network from both topological and functional points of view, this work proposes a methodology that exploits ontology-based biological knowledge for network analysis. To speed up the procedure, an agent-based framework is also presented for supporting distributed computing. The preliminary results show that through the knowledge obtained from gene ontology, our work in analyzing building blocks of PPI networks can give a higher resolution than that of previous ones. Also our agent-based framework can successfully speed up the task of network analysis in an adaptive manner.     

Wafer defect inspection by neural analysis of region features

Wafer defect inspection is an important process that is performed before die packaging. Conventional wafer inspections are usually performed using human visual judgment. A large number of people visually inspect wafers and hand-mark the defective regions. This requires considerable personnel resources and misjudgment may be introduced due to human fatigue. In order to overcome these shortcomings, this study develops an automatic inspection system that can recognize defective LED dies. An artificial neural network is adopted in the inspection. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in the experiments. The results show that the proposed approach successfully identified the defective dies on LED wafers. Personnel costs and misjudgment due to human fatigue can be reduced using the proposed approach.     

DYNAMIC ANALYSES AND ROBUST STEERING CONTROLLER DESIGN FOR AUTOMATED LANE GUIDANCE OF HEAVY‐DUTY VEHICLES

In this paper, we present various linear analyses of the linearized lateral dynamics of heavy‐duty vehicles (HDVs) (tractor‐semitrailer type), which include time domain, frequency domain and pole/zero analyses. These analyses are conducted to examine the vehicle response to the steering input subjected to variations of speed, road adhesion coefficient, cargo load in the trailer, and look‐ahead distance for the lateral deviation sensor. These parameters (uncertainties) have significant influence on vehicle dynamics. It has been shown that redefining the look‐ahead lateral error as the controlled output has a favorable impact on the lateral control problem. Based on these analyses, a robust steering controller using H_∞ loop‐shaping procedure is designed for a tractor semitrailer combination to follow the road center line on both curved and straight highway sections. The proposed controller ensures the robust performance under model uncertainties which include varying vehicle longitudinal speed, road adhesion coefficient, and cargo load in the trailer. The performance of the designed controller is evaluated by simulations and validated by experiments.     

An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles

In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load–deformation behaviour of axially loaded piles. This is a soil–structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile loading tests is used to train and validate the product-unit network. The results show that the proposed hybrid learning algorithm simulates the load–deformation curve of axially loaded piles more accurately than other BP, PSO, and existing PSO–BP hybrid methods. The network developed using the proposed algorithm also turns out to be more accurate than hyperbolic and $t?z$ models.     

Particle Penetration through Inclined and L-Shaped Cracks

This paper presents a particle penetration model predicting particle penetration coefficient (Pp) through a narrow crack of arbitrary incline angles (θ). The objective was to simulate Pp for outdoor-to-indoor particle penetration for residential infiltration conditions. This model assumes laminar infiltration flow and considers particle deposition from both gravitational sedimentation and Brownian diffusion. For micron-sized particles, modeling results indicate that gravitational sedimentation is the major deposition mechanism. Pp increases monotonically with ∣θ∣ because effective particle sedimentation velocity (vs?cos?θ) decreases monotonically with ∣θ∣. For submicron-sized particles (0.1?μm), Brownian diffusion is the major particle deposition mechanism. Because Brownian diffusion is a nondirectional deposition mechanism, crack inclination did not affect Pp. This study applied this model to estimate Pp for L-shaped cracks, and validated modeling results with experiments. Experimental results indicated that inertial impaction and crack entrance cutoff effects were not significant particle deposition mechanisms for the test micron-sized particles. Gravitational sedimentation was the major deposition mechanism. An L-shaped crack can be simulated as the combination of horizontal and vertical sections. This model agreed reasonably with experimental results.     

An expanded SEMATECH CIM framework for heterogeneous applications integration

A significant effort in recent computer-integrated manufacturing (CIM) development was carried out by SEMATECH who had laid out and created an open framework for the integration of manufacturing execution system applications running in semiconductor industries at the factory operation level. This paper presents the feasibility to incorporate basic manufacturing applications at the factory engineering level, which include product design, process planning, and material requirement planning, into the SEMATECH CIM framework to form a generic framework across both factory operation level and factory engineering level. The CIM framework established in this paper was aimed to provide a reusable integrated system framework that clearly specifies the functional interface boundaries and standard information model of the required components, in general, manufacturing systems at both factory operation and factory engineering levels. Standard unified modeling language (UML) diagrams and Petri nets have been utilized to model and analyze the specifications and dynamic behaviors of this generic CIM framework. The goal is to build a framework by creating a common, modular, flexible, and integrated object model that unifies an advanced object-oriented architecture concept and heterogeneous manufacturing application development in an open and multisupplier CIM system environment.