다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The boundary layer equations for plane, incompressible, and steady flow are 

$$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Boundary Layer Theory

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

1. Place animal in induction chamber and anesthetize the mouse and ensure sedation. 2. Once the animal is sedated, move it to a nose cone for hair removal using cream. Only apply cream to the area of the chest that will be utilized for imaging. Once the hair is removed, wipe area with wet gauze to ensure all hair is removed. 3. Move the animal to the imaging platform and tape its paws to the ECG lead plates and insert rectal probe. Body temperature should be maintained at 36-37°C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic.

人唾液天疱疮抗原的纯化

This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work.

/pdf/a-human-assisting-manipulator-teleoperated-by-emg-signals-2b4hdxlktt.pdf

A human-assisting manipulator teleoperated by EMG signals and arm motions

In this paper we introduce a newly developed high-speed multi-fingered robotic hand. The hand has 8-joints and 3-fingers. A newly developed small harmonic drive gear and a high-power mini actuator are fitted in each finger link, and a strain gauge sensor is in each joint. The weight of the hand module is only 0.8 kg, but high-speed motion and high-power grasping are possible. The hand can close its joints at 180 deg per 0.1 s, and the fingertips have an output force of about 28 N. The hand system is controlled by a massively parallel vision system. Experimental results are shown in which a falling object was caught by the high-speed hand.

Development of a high-speed multifingered hand system and its application to catching

This paper proposes a new active sensor system (active antenna) that can detect not only the contact location between an insensitive flexible beam and an environment but also the information of the environment's surface where the beam makes contact. The active antenna is simply composed of a flexible beam, actuators to move the beam, position sensors to measure the rotational angle of the beam, and a moment sensor. We first show that the contact distance under no lateral slip is proportional to the rotational compliance that the beam can sense at the rotational center. The lateral slip, which possibly occurs according to the pushing direction and the environment's geometry, overestimates the rotational compliance, and as a result, brings a large sensing error for the localizing contact point. The goal of this paper is to find the contact location under such conditions. We explore how to detect a lateral slip and how to determine the new pushing direction to avoid it. We show an algorithm that can search for the pushing direction which can avoid any lateral slip. The convergence of this algorithm is shown and a practical utilization of this algorithm is also discussed with a trade-off between the number of trials and the sensing accuracy.

/pdf/active-antenna-for-contact-sensing-mwdalee5cd.pdf

Active antenna for contact sensing

A finger-shaped tactile sensor previously reported in a scaled-up version was miniaturized and its signal processing speed improved. The miniaturized tactile sensor consists of a hemispherical optical waveguide with an elastic cover, a fiber-optics plate (FOP), a position-sensitive detector (PSD), and infrared light-emitting diodes (LEDs) for the light source. When an object comes in contact with the sensor, the elastic cover is depressed and light from the LEDs, which normally maintains total internal reflection within the waveguide, is scattered at the contact location of the waveguide and the cover. The scattered light is transmitted to the PSD through the FOP. The contact location and the normal to the surface of the object are determined from the output of the PSD. The sensor's fundamental characteristics were evaluated through experiments using the miniaturized version. The sensor was installed at the tip of a robotic finger to create an active touch system. By combining tactile sensing and motion control of the finger, the profile of an unseen object was successfully delineated. >

Development of a finger-shaped tactile sensor and its evaluation by active touch

Proposes a new probabilistic neural network (NN) that can estimate the a-posteriori probability for a pattern classification problem. The structure of the proposed network is based on a statistical model composed by a mixture of log-linearized Gaussian components. However, the forward calculation and the backward learning rule can be defined in the same manner as the error backpropagation NN. In this paper, the proposed network is applied to the electroencephalogram (EEG) pattern classification problem. In the experiments described, two types of a photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. It is shown that the EEG signals can be classified successfully and that the classification rates change depending on the amount of training data and the dimension of the feature vectors.

/pdf/a-log-linearized-gaussian-mixture-network-and-its-wd9zhi09zw.pdf

Makoto Kaneko

Papers

A human-assisting manipulator teleoperated by EMG signals and arm motions

Development of a high-speed multifingered hand system and its application to catching

Active antenna for contact sensing

Development of a finger-shaped tactile sensor and its evaluation by active touch

A log-linearized Gaussian mixture network and its application to EEG pattern classification