Yu-Te Su

TLR2-dependent selective autophagy regulates NF-κB lysosomal degradation in hepatoma-derived M2 macrophage differentiation

Autophagy is a lysosomal pathway for cellular homeostasis control. Both non-selective bulk autophagy and selective autophagy of specific proteins or organelles have been found. Selective autophagy prevents cells from pathogen invasion and stress damage, but its role in regulating transcriptional factors is not clear. Using a macrophage cell differentiation model, the role of autophagy in nuclear factor-κB (NF-κB) regulation is investigated. The bone marrow-derived macrophages (BMDMs) will differentiate into a M2-like phenotype in the presence of hepatoma tumor cell condition medium (CM). The TLR2 signaling drives this M2 polarization and causes NF-κB p65 degradation via lysosome-dependent pathway. The CM-induced ubiquitinated- NF-κB p65 forms aggresome-like structures (ALS) in the cytoplasm of cultured and hepatoma-associated M2 macrophages. This NF-κB p65-contained ALS is recognized by p62/SQSTM1 and degraded by selective autophagy. Treatment with the lysosomal inhibitor bafilomycin A1 or the knockdown of Atg5 can prevent CM-induced NK-κB p65 degradation and induce M2 macrophages to produce a high level of pro-inflammatory cytokines. Furthermore, TLR2 signal induces sustained phosphorylation of extracellular signal-regulated kinase 1/2 to facilitate this autophagy-dependent NF-κB regulation. Our finding provides a novel pathway of NF-κB regulation by p62/SQSTM1-mediated selective autophagy.

Walking Motion Generation, Synthesis, and Control for Biped Robot by Using PGRL, LPI, and Fuzzy Logic

This paper proposes the implementation of fuzzy motion control based on reinforcement learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots. First, the procedure of a walking gait is redefined into three states, and the parameters of this designed walking gait are determined. Then, the machine learning approach applied to adjusting the walking parameters is policy gradient RL (PGRL), which can execute real-time performance and directly modify the policy without calculating the dynamic function. Given a parameterized walking motion designed for biped robots, the PGRL algorithm automatically searches the set of possible parameters and finds the fastest possible walking motion. The reward function mainly considered is first the walking speed, which can be estimated from the vision system. However, the experiment illustrates that there are some stability problems in this kind of learning process. To solve these problems, the desired zero moment point trajectory is added to the reward function. The results show that the robot not only has more stable walking but also increases its walking speed after learning. This is more effective and attractive than manual trial-and-error tuning. LPI, moreover, is employed to transform the existing motions to the motion which has a revised angle determined by the fuzzy motion controller. Then, the biped robot can continuously walk in any desired direction through this fuzzy motion control. Finally, the fuzzy-based gait synthesis control is demonstrated by tasks and point- and line-target tracking. The experiments show the feasibility and effectiveness of gait learning with PGRL and the practicability of the proposed fuzzy motion control scheme.

Optical image stabilizing system using fuzzy sliding-mode controller for digital cameras

An optical image stabilizing (OIS) system using fuzzy sliding-mode controller (FSMC) is presented in this research. The OIS system shifts the image sensor to correct the deviation from the optical path caused by a users handshake vibration. The voice coil motors (VCM) was adopted to drive the OIS module which bears the image sensor, a gyroscope was used to detect the hand-shake vibration, and two single-axis hall sensors were designed to sense the position of the VCM actuators. The proposed FSMC overcomes the nonlinear, hysteresis, and time-varying properties of the VCM actuator efficiently. The stabilizing performance was demonstrated by digital photographs taken under different test conditions.

Design and Implementation of Fuzzy Policy Gradient Gait Learning Method for Walking Pattern Generation of Humanoid Robots

The design and implementation of Fuzzy Policy Gradient Learning (FPGL) method for humanoid robot is proposed in this paper. This paper not only introduces the phases of the humanoid robot walking, but also improves and parameterizes the gait pattern of the robot. FPGL is an integrated machine learning method based on Policy Gradient Reinforcement Learning (PGRL) and fuzzy logic concept in order to improve the efficiency and speed of gait learning computation. The result of the experiment shows that FPGL method can train the gait pattern from 9.26 mm/s walking speed to 162.27 mm/s within an hour. The training data of experiments also shows that this method could improve the efficiency of basic PGRL method up to 13%. The effect of arm movement to reduce the tilt of the trunk is also proved by the experimental results. All the results successfully demonstrate the feasibility and the flexibility of the proposed method.

Stair-Climbing Control of Humanoid Robot using Force and Accelerometer Sensors

In this paper, an autonomous control scheme is proposed for humanoid robot to accomplish the stair-climbing mission. At first, we design a fuzzy based auto-balance gait controller and adopt the zero moment point (ZMP) criterion to construct the basic motion patterns. Next we propose a ten-step stair-climbing control scheme. For acquiring the information from the environment, two kinds of sensors, the accelerometer and the force sensor are utilized. Experimental results demonstrate that the developed humanoid robot can successfully accomplish the stair-climbing mission though stair steps are with different height.

Omni-Directional Vision-Based Control Strategy for Humanoid Soccer Robot

The humanoid soccer robot system can be considered as a visual feedback system that includes the design and implementation of the hardware architecture, visual servo system, control strategy system and sensory system. This paper is mainly to integrate the two major parts of the humanoid soccer robot, the visual system and control strategy system. At first, the architecture of the omni-directional vision system is setup. The advanced label algorithm (ALA) is then employed for object recognition in the vision processing system. The corresponding offending and defending control strategies are developed. Two special data transformation mechanisms are also examined. Finally the experiments of the humanoid soccer robot are performed to verify the benefit and the feasibility of the proposed control strategies.

SOPC based weight lifting control design for small-sized humanoid robot

An SOPC based small-sized humanoid robot for weight lifting is introduced in this paper. All performances will be implemented using the SOPC chip, Altera EP1C12F324C8. The contribution of this paper is mainly the design of the self-balanced control of a small sized humanoid robot. According to the concept of sensory reflex, we combine the signals of the accelerometers and force sensors with a fuzzy controller to design a dynamic balanced controller for the humanoid robot. Through the implementation of the controller, we can strengthen the activity stability and the robustness of adapting to lift the weight. Finally, the experiment results show that the robot can successfully execute weight lifting function.

Design and implementation of penalty kick function for small-sized humanoid robot by using FPGA

This paper mainly concerns about the development of a small-sized humanoid robot for penalty kick function in FIRA by using FPGA. The environment information is captured by the CMOS image sensor and the strategy is processed by FPGA. We will describe how the robot search the ball in the field and the tracking algorithm will be introduced. The strategy for PK event will also be presented in this paper. The contribution of this paper is the implementation of the PK strategy for a small-sized humanoid robot.

FPGA-based fuzzy PK controller and image processing system for small-sized humanoid robot

This paper mainly covers the development of a FPGA-based fuzzy controller and image processing system for a small-sized humanoid robot. All the computations are operated on an FPGA board including the real-time image processing and the fuzzy logic controller design for PK event in FIRA RoboWorld cup. At first, the specification of the hardware is introduced. The image processing is then employed for target recognition. The control strategy system for PK event is also developed. Finally the experiments are demonstrated to verify feasibility of the proposed control system.

A novel ecological-biological-behavior praticle swarm optimization for Ackley's function

Basing on the simulation of a social metaphor instead of the survival of the fittest individual paradigm, particle swarm optimization is a population-based swarm intelligence algorithm. Inspired by the conventional particle swarm method, ecological theory, and biological theory, this work proposes a novel ecological-biological-behaved particle swarm optimization (EBB-PSO) algorithm using logical growth model with symbiotic relationship. The simulation results demonstrate good performance of the proposed algorithm in solving a significant benchmark problem in multi-modal function, namely the Ackleys problem.