Exercise-induced myokines in health and metabolic diseases

Skeletal muscle has been emerging as a research field since the past 2 decades. Contraction of a muscle, which acts as a secretory organ, stimulates production, secretion, and expression of cytokines or other muscle fiber-derived peptides, i.e., myokines. Exercise-induced myokines influence crosstalk between different organs in an autocrine, endocrine, or paracrine fashion. Myokines are recently recognized as potential candidates for treating metabolic diseases through their ability to stimulate AMP-activated protein kinase signaling, increase glucose uptake, and improve lipolysis. Myokines may have positive effects on metabolic disorders, type 2 diabetes, or obesity. Numerous studies on myokines suggested that myokines offer a potential treatment option for preventing metabolic diseases. This review summarizes the current understanding of the positive effects of exercise-induced myokines, such as interleukin-15, brain-derived neurotrophic factor, leukemia inhibitory factor, irisin, fibroblast growth factor 21, and secreted protein acidic and rich in cysteine, on metabolic diseases

A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning

With the rapid increase in the number of cyber-attacks, detecting and preventing malicious behavior has become more important than ever before. In this study, we propose a method for detecting and classifying malicious behavior in host process data using machine learning algorithms. One of the challenges in this study is dealing with high-dimensional and imbalanced data. To address this, we first preprocessed the data using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to reduce the dimensions of the data and visualize the distribution. We then used the Adaptive Synthetic (ADASYN) and Synthetic Minority Over-sampling Technique (SMOTE) to handle the imbalanced data. We trained and evaluated the performance of the models using various machine learning algorithms, such as K-Nearest Neighbor, Naive Bayes, Random Forest, Autoencoder, and Memory-Augmented Deep Autoencoder (MemAE). Our results show that the preprocessed datasets using both ADASYN and SMOTE significantly improved the performance of all models, achieving higher precision, recall, and F1-Score values. Notably, the best performance was obtained when using the preprocessed dataset (SMOTE) with the MemAE model, yielding an F1-Score of 1.00. The evaluation was also conducted by measuring the Area Under the Receiver Operating Characteristic Curve (AUROC), which showed that all models performed well with an AUROC of over 90%. Our proposed method provides a promising approach for detecting and classifying malicious behavior in host process data using machine learning algorithms, which can be used in various fields such as anomaly detection and medical diagnosis

Adaptive flow separation control over an asymmetric airfoil

A closed-loop adaptive flow control system was experimentally demonstrated to control the flow separation over an NACA 64A210 airfoil. To control the flow separation, piezoelectrically driven synthetic jet actuators and pressure sensors were used. Before performing the closed-loop flow separation control experiment, the effectiveness of the synthetic jet actuator was investigated by open-loop experiments for an NACA 64A210 airfoil in various operating conditions to examine the effective control parameter. The pressure gradient, which was calculated from the difference of mean pressure coefficients between two sensor positions, was found to be a criterion for flow recovery in these experiments. Therefore, the pressure gradient was selected as a control parameter for separated flow. An adaptive flow control system using adaptive inverse control and extremum-seeking control was developed to control the pressure gradient for separated flow over an NACA 64A210 airfoil. The performance of the resulting flow control system was compared with a simple MD control system. Experimental results based on the proposed adaptive flow control system demonstrated the satisfactory tracking performance of the pressure gradient for the pressure recovery. As a result, the proposed adaptive flow control approach enhanced the aerodynamics performances in terms of lift and drag coefficients and lift/drag ratio in the closed-loop control system.OAIID:RECH_ACHV_DSTSH_NO:T201812608RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A001138CITE_RATE:.483DEPT_NM:기계항공공학부EMAIL:[email protected]_YN:YN

CNTK-based multi-task learning for gender and age recognition using face images

Since the success of deep learning in numerous contests in pattern recognition and machine learning, it has already been used in widespread fields such as speech recognition, medical diagnosis and unmanned vehicle. The improvement of computing facilities, big data and sophisticated deep learning techniques have led to the success of deep learning. In addition, the deep learning software such as Caffe, Theano, Torch and etc. also lead to the popularization of deep learning. In this paper, we explored the CNTK (computational network toolkit or Microsoft cognitive toolkit), that is a deep learning software provided by Microsoft. The CNTK is a general purpose machine learning software that can support multi-devices and multi-GPUs. That can model various neural network architectures such as deep neural network, convolutional neural network and recurrent neural network. Also, researcher can easily design and evaluate various neural network architectures using BrainScript, C++ and Python. We used the CNTK for gender and age recognition by face images with multi-task learning. Face images contain rich information such as identity, expression, age, gender and races. It is helpful to extract multiple information simultaneously from face images for developing applications. In addition, it is observed that multi-task learning may help to improve the performance than single-task learning. We modeled the simple multilayer perceptron network for gender and age recognition. And then we applied multi-task learning with a same network. For the computational evaluation, we collected a set of face images from FERET (the facial recognition technology) database for gender and age recognition. In the computational experiments, we confirmed that the performance of multi-task learning is slightly better than single-task learning. We also confirmed that it is easy to model the neural network architecture using the CNTK

Real-Time Hand Detection From a Single Depth Image by Per-Pixel Classification

Due to their convenience and naturalness, hand pose recogni-tion or gesture recognition methods are gaining attention as an upcom-ing complement of traditional input devices such as keyboards, mice, joysticks, etc. Robust hand detection from an image is the first stage to solve the hand gesture recognition. Due to the release of the commer-cial depth camera, elimination of the cluttered background from a depth image is much easier than from a RGB image. However, accurate hand segmentation from a human body still remains in challenging task. Here, we propose robust real-time hand detection algorithm from a depth im-age. The algorithm is designed to detect hands with various hand poses in various positions in 3D space. We train Radom Decision Forests to every pixel in the image to detect hand. The pixel in the image has one of the two label, hand or non-hand. We optimize the random decision forests parameters by various experimental conditions. The result shows that the per-pixel classification accuracy is 94% and the RDF with 5 trees requires only 12ms with no help of parallel programming

Development of Chemiluminescence Resonance Test System Using SiPM Front-end ASIC to Detect Na and K Ions in Urine

The importance of measure and control dietary salinity arises to prevent and control the disease. There are several methods to measure the dietary salinities from blood or urine. The blood test is an accurate but inconvenient method because patients need to be at hospitals and wait for a longer time. Urine can be collected at home, and the test is more convenient. A 24-hour urine test is more accurate than random urine (RU) may cause more human errors. For this reason, testing RU accuracy for application will increase the convenience of patients. A SiPM sensor system to measure Guanine-based chemiluminescence resonance test light was developed. An ASIC system was developed and packaged to a chip. A test board for the packaged chip was developed. In parallel, the layout of an ASIC chip was assembled with SiPM and tested in the dark chamber to understand the functionality. The ASIC chip was tested in various frequencies with the test board. At the target frequency, the ASIC chip achieved 870 gain, which is exceeding the goal of 100. The SiPM system was measured with an oscilloscope, and the output signal was as expected. The performance test was done at a very high frequency (100MHz) and achieved 80.5% detection compared to the original light source signal. The ASIC chip development was successful, and SiPM matched the specification of the target operation

Facial Expression Recognition using Region-SIFT

Facial expression recognition is a challenging field in numerous re-searches, and impacts important applications in many areas such as human–computer interaction and data-driven animation, etc. In this paper, we propose facial expression recognition system, which consists of three process. First step is face alignment using active shape model (ASM). Second step is feature extraction step. Because of high recogni-tion rate, Scale Invariant Feature Transform (SIFT) feature extraction method is widely applied to many recognition problems. However, it takes a long processing time. In order to solve the problem, we intro-duce Region-SIFT feature extraction method based on facial landmark. In this process, we define the sub-region of face, such as eye, nose, and mouth. And then we extract SIFT feature from each sub-region. In or-der to reduce dimension of feature we employ Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, it is connected to non-linear Support Vector Machines (SVM) for efficient classification. The performance evaluation of proposed method was performed with the CK facial expression database and JAFFE data-base. The experimental results demonstrate that our proposed ap-proach is very efficient to classify the facial expression. As a result, the method using Region-SIFT showed performance improvements of 14.52% and 19.75% compared to previous method using SIFT for CK database and JAFFE database, respectively

Design of a Personallized Sportainment Treadmill using Mixed-Reality Contents

In this paper, we propose a new concept of treadmill, that is, personalized sportainment treadmill. The personalized sportainment treadmill is a new system which gives personalized entertainment services in a treadmill. It can be configured with user recognition technology, mixed reality-based interactive content, mobile-treadmill synchronization technology, and more. Among them, user recognition technology and the mixed reality-based interactive contents are very important for users to give an immersive experience. So, we designed and proposed face recognition, gesture recognition, interactive virtual trainer and gamification technologies in this paper. With these technologies, the personalized sportainment treadmill system will give a immersive experience to users