A Meta-Analysis of Ethical Fashion Consumption Research in South Korea

In this study, a meta-analysis of studies on ethical fashion consumption in South Korea was conducted with the purpose of better understanding the influences of different factors on ethical fashion consumption

A systemic review and meta-analysis on the antihypertensive effect of aromatherapy essential oils

Purpose: This study is a systemic review of experimental results on the effects of aromatherapy on blood pressure. Materials and Methods: Journal articles published to December, 2017, were retrieved from twelve databases. Randomized controlled trials in which were evaluated for changes in blood pressure following aromatherapy were selected. Risks of bias were assessed using the risk-of-bias (ROB) tool of the Cochrane Collaboration. Meta-analysis were dine using RevMan. Results: Of the 2545 articles retrieved from the electronic databases, 580 duplicate articles and 1891 articles that were unrelated to the PICO (patient/problem, intervention, comparison, outcome) elements or did not satisfy the inclusion criteria excluded. Of the remaining 74 articles, 15 found to satisfy the inclusion criteria after full-text review and therefore selected for analysis. The findings of meta-analysis of 11 of these 15 articles revealed that essential oil inhalation and massage effectively decreased both systolic (n = 379; mean difference [MD], -4.72; 95% confidence interval [CI], -8.38 to -1.07) and diastolic (n = 379; MD, -2.42; 95% CI, -4.46 to -0.38) pressure. Conclusions: Essential oil inhalation and massage therapy can effectively decrease systolic and diastolic pressure in health adults as well as in patients with hypertension

Visual Recognition for Autonomous Vehicles

Department of Electrical EngineeringOver the past several years, the computer vision community has been researching object detection and classification. These can be applied many fields, but are particularly important to autonomous car development. In initial work in this area, many researchers sought higher accuracy on images. The recently developed Convolutional Neural Network (CNN) stands as great achievement. However, its training model is only suitable for photos, because its training data consists of many photos. To achieve higher performance on dash-cam videos, we need to acquire and process voluminous dash-cam training data for suitable training. For supervised learning, each object must be labelledhowever, is the computation cost for this is very high because the video has many frames and many objects. For reducing cost of labeling and automatically increasing object detection and classification system accuracy, the present study proposes incremental learning for an object recognition system. The proposed model is based on a Faster R-CNN (Regions with Convolutional Neural Network features) model for object detection and recognition. My main idea can be divided into two parts. The first is detection with object tracking. Faster R-CNN???s object detector depends solely on an object proposal network. Thus, sometimes it ignores many objects. In the proposed method, this shortcoming is overcome using video???s feature. Video has a time domain, and previous and current frames have correlation of visualized objects. Accordingly, an object tracker is used. Summation of object proposal and object tracking results yields remarkable progress in object detection. The second is object classification and incremental learning. Previous works train a model using training data, and they focus on optimizing the model with training data. Thus, to improve the accuracy or add training data, we should use a re-training model. This method requires a division of time between training and testing. Thus, a simultaneous learning system for real-time to improve accuracy is proposed here. If inputted data???s output object score is higher than a threshold, then we retain this object for reuse as training data. This method makes mAP improvement effects. For availability of autonomous vehicles, security is paramount. State-of-the-art works are based on sensor or simple visual detection. For example, distance between vehicles is detected using the radar, and lane departure detection is done using a line detection algorithm. However, this is just a single functionit can detect head-on collisions but cannot execute sufficiently rapidly for dangerous situations. So, a hazard or accident prediction and accident categorization system is proposed here. The proposed system can detect a variety of hazards and accidents, and it can categorize kinds of accident, not only involving the user???s car but also between others. The proposed system focuses on giving warning information to drivers. The algorithm used here is based on a CNN-LSTM model with many accident videos. Two models are used for this system. The first is a hazard or accident detection model. This model attaches the label of accident probability, and final loss layer is the Euclidean loss layer for model training. This enables it to detect the probability of hazard or accident, and if its output is over the threshold then a hazard is identified and then input into the accident categorization model. Second is an accident categorization model. My system can detect nine kinds of accidents: forward, cut in car accident, intersection accident, jay walking, collision with two-wheeled vehicle, collision between another vehicle and a two-wheeled vehicle, another car slipping, another car reversing, and rollover accident of another car. The algorithm uses a max-pooling layer after LSTM layers for feature information propagation of having striking features.clos

A Meta-Analysis of Ethical Fashion Consumption Research in South Korea

In this study, a meta-analysis of studies on ethical fashion consumption in South Korea was conducted with the purpose of better understanding the influences of different factors on ethical fashion consumption.</p

Video-based emotion identification using face alignment and support vector machines

This abstract introduces an efficient method for identifying various facial expressions from image inputs. To recognize the emotions of the facial expressions, a number of facial feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by a support vector machine (SVM). Based on the experimental results, SVM performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions

Facial emotion recognition using active shape models and statistical pattern recognizers

This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional feature vector for each still image. Statistical pattern recognizers, such as support vector machine (SVM) and multi-layer perceptron (MLP), are used to identify various emotions from the feature vectors. To analyze the performances of the various pattern recognizers on the limited amount of image data, 5-fold cross-validation is carried out, with varying numbers of emotions from 3 to 6. Evaluation results show that SVM is the most stable and best in terms of emotion classification rates

Acupuncture Treatment for Restless Legs Syndrome: A Review of Randomized Controlled Trials

To determine the effectiveness of acupuncture in treating restless legs syndrome (RLS), we conducted a literature review of randomized controlled trials (RCTs) that utilized acupuncture as an intervention for patients diagnosed with RLS. Relevant clinical studies (n = 158) from seven databases (the Cochrane Library, PubMed, Embase, CNKI, KISS, RISS, and OASIS) were included based on the inclusion and exclusion criteria and analyzed. Moreover, 6 RCTs were selected for review. In all six studies, it was indicated people who underwent acupuncture treatment showed significant improvements in their overall health. An increase in the treatment efficacy rate, sleep quality, and quality of life indicators after the acupuncture treatment was confirmed. The severity of pain as assessed using the visual analog scale (VAS) scores and International RLS Study Group Rating Scale (IRLSRS) scores and the severity of RLS symptoms were significantly reduced. Any significant side effects were not reported. Acupuncture is suggested as an effective and safe treatment method for RLS. However, further large-scale RCT studies are needed to confirm our findings

Electroacupuncture Treatment for Post-Stroke Foot Drop: A Systemic Review of Randomized Controlled Trials

A review of randomized controlled trials (RCTs) using electroacupuncture (EA) to treat patients with foot drop was performed to analyze the effectiveness of EA for this condition. Relevant studies (n = 183) from 7 databases (Cochrane Library, Excerpta Medica Database, PubMed, China National Knowledge Infrastructure, Korean Studies Information Service System, Research Information Sharing Service, and Oriental Medicine Advanced Searching Integrated System) were selected based on the inclusion and exclusion criteria, and 12 RCTs met the selection criteria. In all 12 studies, EA showed significantly positive changes. In most indicators, positive changes were observed in the EA group compared with that in the control group. Significant increases were confirmed in muscle strength-related indicators such as the Fugl–Meyer motor scale, surface electromyography, active range of motion, and gait-related indicators such as the Tinetti score, maximum walking speed, and Berg balance scale. No notable adverse events were reported. EA is suggested as an effective treatment for post-stroke foot drop; however, more RCTs are required

Occlusion detection using horizontally segmented windows for vehicle tracking

This paper proposes an efficient algorithm for detecting occlusions in a video sequences of ground vehicles using color information. The proposed method uses a rectangular window to track a target vehicle, and the window is horizontally divided into several sub-regions of equal width. Each region is determined to be occluded or not based on the color histogram similarity to the corresponding region of the target. The occlusion detection results are used in likelihood computation of the conventional tracking algorithm based on particle filtering. Experimental results in real scenes show that the proposed method finds the occluded region successfully and improves the performance of the conventional trackers.close1