Economic and Political Changes and Import Demand Behavior of North Korea

We study some empirical aspects of North Korean economy implied in its import behavior in the period before the collapse of Soviet Union. Our analysis is based on econometric inference for a cointegration relation and some model determination methods. We have found that for North Korean economy some non-market factors are important determinants of the import behavior. The non-market factors are related to the country¡¯s political situations, its political relation with two communist superpowers, and its relation with western industrialized countries. Our results show that the non-market factors have different impacts on imports from different countries and imports for different commodity groups, which enables us to find some interesting aspects of North Korean economy. Among several results those with the following two implications are of particular interests. First, the two communist superpowers were overall stable and the most important suppliers to North Korean economy regardless of the political situation while Western countries filled the deficiency, if any, caused by Sino-Soviet dispute. Second, the foreign debt problem had significantly negative impacts on imports from the capitalist countries, which is true even after the new open door policy initiated in 1984.

Economic and Political Changes and Import Behavior of North Korea

Determinants of Imports, Non-market factors, Stable dynamic system.

Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features

Emotion recognition research has been conducted using various physiological signals. In this paper, we propose an efficient photoplethysmogram-based method that fuses the deep features extracted by two deep convolutional neural networks and the statistical features selected by Pearson’s correlation technique. A photoplethysmogram (PPG) signal can be easily obtained through many devices, and the procedure for recording this signal is simpler than that for other physiological signals. The normal-to-normal (NN) interval values of heart rate variability (HRV) were utilized to extract the time domain features, and the normalized PPG signal was used to acquire the frequency domain features. Then, we selected features that correlated highly with an emotion through Pearson’s correlation. These statistical features were fused with deep-learning features extracted from a convolutional neural network (CNN). The PPG signal and the NN interval were used as the inputs of the CNN to extract the features, and the total concatenated features were utilized to classify the valence and the arousal, which are the basic parameters of emotion. The Database for Emotion Analysis using Physiological signals (DEAP) was chosen for the experiment, and the results demonstrated that the proposed method achieved a noticeable performance with a short recognition interval

MIFT: A Moment-Based Local Feature Extraction Algorithm

We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods

Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection

Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks

A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

A new obstacle avoidance method for autonomous vehicles called obstacle-dependent Gaussian potential field (ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases

Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions

The biologically inspired hierarchical model for object recognition, Hierarchical Model and X (HMAX), has attracted considerable attention in recent years. HMAX is robust (i.e. shift‐ and scale‐invariant), but its use of random‐patch‐selection makes it sensitive to rotational deformation, which heavily limits its performance in object recognition. The main reason is that numerous randomly chosen patches are often orientation selective, thereby leading to mismatch. To address this issue, the authors propose a novel patch selection method for HMAX called saliency and keypoint‐based patch selection (SKPS), which is based on a saliency (attention) mechanism and multi‐scale keypoints. In contrast to the conventional random‐patch‐selection‐based HMAX model that involves huge amounts of redundant information in feature extraction, the SKPS‐based HMAX model (S‐HMAX) extracts a very few features while offering promising distinctiveness. To show the effectiveness of S‐HMAX, the authors apply it to object categorisation and conduct experiments on the CalTech101, TU Darmstadt, ImageNet and GRAZ01 databases. The experimental results demonstrate that S‐HMAX outperforms conventional HMAX and is very comparable with existing architectures that have a similar framework

Multiple Object Tracking Using Re-Identification Model with Attention Module

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system