Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map.

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image-acquired using the camera of a mobile device-via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

Antibacterial and anti-biofilm activity, and mechanism of action of pleurocidin against drug resistant Staphylococcus aureus.

The abuse of antibiotics has resulted in the emergence of multi-drug-resistant bacteria. Staphylococcus aureus is a frequent cause of infections, and antibiotic-resistant S. aureus has become a serious problem. Antimicrobial peptides play an important role in innate immunity and are attracting increasing attention as alternative antibiotics. In a previous study, pleurocidin, derived from winter flounder, was identified as a 25-amino acid antimicrobial peptide with no cytotoxicity toward mammalian cells and low hemolytic activity. In the present study, pleurocidin was observed to exhibit antimicrobial activity against gram-positive and gram-negative bacteria, especially against drug resistant S. aureus. Pleurocidin retained its antibacterial activity against drug resistant S. aureus in the presence of a physiological salt concentration. Membrane depolarization assays and propidium iodide uptake indicated that pleurocidin kills bacteria by damaging the integrity of the bacterial membrane. DNA binding assays revealed that pleurocidin binds to DNA. Thus, pleurocidin targets not only the bacterial membrane, but also their DNA. S. aureus biofilms have become a serious problem because of increased resistance to antibiotics. Therefore, we investigated the effect of pleurocidin on biofilm inhibition and eradication using crystal violet staining and microscopic observation. Pleurocidin inhibited and eradicated biofilms at low concentrations. Taken together, the results suggested that pleurocidin is a promising candidate therapeutic agent to treat drug-resistant bacteria and biofilm-related infections.

Towards a Meaningful 3D Map Using a 3D Lidar and a Camera.

Semantic 3D maps are required for various applications including robot navigation and surveying, and their importance has significantly increased. Generally, existing studies on semantic mapping were camera-based approaches that could not be operated in large-scale environments owing to their computational burden. Recently, a method of combining a 3D Lidar with a camera was introduced to address this problem, and a 3D Lidar and a camera were also utilized for semantic 3D mapping. In this study, our algorithm consists of semantic mapping and map refinement. In the semantic mapping, a GPS and an IMU are integrated to estimate the odometry of the system, and subsequently, the point clouds measured from a 3D Lidar are registered by using this information. Furthermore, we use the latest CNN-based semantic segmentation to obtain semantic information on the surrounding environment. To integrate the point cloud with semantic information, we developed incremental semantic labeling including coordinate alignment, error minimization, and semantic information fusion. Additionally, to improve the quality of the generated semantic map, the map refinement is processed in a batch. It enhances the spatial distribution of labels and removes traces produced by moving vehicles effectively. We conduct experiments on challenging sequences to demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and intersection over union.

An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver.

This paper proposes a real-time position accuracy improvement method for a low-cost global positioning system (GPS), which uses geographic data for forming a digital road database in the digital map information. We link the vehicle's location to the position on the digital map using the map-matching algorithm to improve the position accuracy. In the proposed method, we can distinguish the vehicle direction on the road and enhance the horizontal accuracy using the geographic data composed of the vector point set of the digital map. We use the iterative closest point (ICP) algorithm that calculates the rotation matrix and the translation vector to compensate for the disparity between the GPS and the digital map information. We also use the least squares method to correct the error caused by the rotation of the ICP algorithm and link on the digital map to eliminate the residual disparity. Finally, we implement the proposed method in real time with a low-cost embedded system and demonstrate the effectiveness of the proposed method through various experiments.

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

Application of cross time-frequency analysis to postural sway behavior: the effects of aging and visual systems.

In this paper, the effects of visual feedback and aging on postural sway systems and signals are investigated by analyzing the transient phase difference between "input" and "output" which correspond to center of pressure (COP) and center of mass (COM), respectively. In order to analyze the transient phase difference characteristics of COP and COM, a relatively new cross time-frequency analysis technique that provides time- and frequency-localized phase difference information is utilized. The feedback control process in the postural sway is interpreted in terms of a feedback compensator which is characterized in terms of a phase difference. Using the experimental results of the transient phase difference obtained from the cross time-frequency distribution, it is demonstrated that the postural control of young persons are more stable and rely more on visual sensory feedback to stabilize postural control compared to that of the elderly persons.

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

Real-time emergency telemedicine system: prototype design and functional evaluation.

In this paper, an emergency telemedicine system was designed for the transmission of real-time multimedia for remote consultation, including radiological images, patient records, video-conferencing, full-quality video, ECG, BP, respiration, temperature, SpO(2), systolic and diastolic pressures and heart rate. The standardized, modular, software-based design architecture, without resorting to external hardware compression boards, enables the low-cost implementation of the telemedicine system, using the unified, systematic and compact integration of multimedia on general personal computers. Experimental tests on local networks analyze the technical aspects of designed systems, and inter-hospital experiments demonstrate its clinical usefulness.

Comments on "Output tracking and regulation of nonlinear system based on Takagi-Sugeno fuzzy model".

In the above paper, a Takagi-Sugeno (T-S) fuzzy-model-based output regulation methodology is proposed. However, since the problem formulation is incorrect, the derived design techniques may not accomplish the control objectives. We illustrate the reasons and show counterexamples.

Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays.

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.

Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays.

This brief proposes a simple control approach for a class of uncertain nonlinear systems with unknown time delays in strict-feedback form. That is, the dynamic surface control technique, which can solve the "explosion of complexity" problem in the backstepping design procedure, is extended to nonlinear systems with unknown time delays. The unknown time-delay effects are removed by using appropriate Lyapunov-Krasovskii functionals, and the uncertain nonlinear terms generated by this procedure as well as model uncertainties are approximated by the function approximation technique using neural networks. In addition, the bounds of external disturbances are estimated by the adaptive technique. From the Lyapunov stability theorem, we prove that all signals in the closed-loop system are semiglobally uniformly bounded. Finally, we present simulation results to validate the effectiveness of the proposed approach.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.