Yunsick Sung

Unmanned Aerial Vehicle Flight Point Classification Algorithm Based on Symmetric Big Data

Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.

A Development Architecture for Serious Games Using BCI (Brain Computer Interface) Sensors

Games that use brainwaves via brain–computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification

BackgroundAn imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class.ResultsIn this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE.ConclusionsOur proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

Graph-based motor primitive generation framework

Unmanned aerial vehicles (UAVs) have many potential applications, such as delivery, leisure, and surveillance. To enable these applications, making the UAVs fly autonomously is the key issue, and requires defining UAV motor primitives. Diverse attempts have been made to automatically generate motor primitives for UAVs and robots. However, given that UAVs usually do not fly as expected because of external environmental factors, a novel approach for UAVs needs to be designed. This paper proposes a demonstration-based method that generates a graph-based motor primitive. In the experiment, an AR.Drone 2.0 was utilized. By controlling the AR.Drone 2.0, four motor primitives are generated and combined as a graph-based motor primitive. The generated motor primitives can be performed by a planner or a learner, such as a hierarchical task network or Q-learning. By defining the executable conditions of the motor primitives based on measured properties, the movements of the graph-based motor primitive can be chosen depending on changes in the indoor environment.

A Framework for Processing Brain Waves Used in a Brain-computer Interface

Recently, methodologies for developing brain-computer interface (BCI) games using the BCI have been actively researched. The existing general framework for processing brain waves does not provide the functions required to develop BCI games. Thus, developing BCI games is difficult and requires a large amount of time. Effective BCI game development requires a BCI game framework. Therefore the BCI game framework should provide the functions to generate discrete values, events, and converted waves considering the difference between the brain waves of users and the BCIs of those. In this paper, BCI game frameworks for processing brain waves for BCI games are proposed. A variety of processes for converting brain waves to apply the measured brain waves to the games are also proposed. In an experiment the frameworks proposed were applied to a BCI game for visual perception training. Furthermore, it was verified that the time required for BCI game development was reduced when the framework proposed in the experiment was applied

Beacon-based active media control interface in indoor ubiquitous computing environment

In ubiquitous computing, diverse media technologies have recently been extensively researched and applied to various fields. The goal of media technologies is to improve our daily lives by enabling us to control active media devices such as smart phones, tablets, and TVs. The locations of users are important to providing a variety of services to users. In addition, given that all devices cannot be simultaneously utilized, and only one device is sometimes being used, the location information can be utilized to help determine one of the core active media devices at a specific instant in time. This paper proposes an active media-control interface that is based on location recognition methods using beacons in indoor ubiquitous computing. In the proposed environment, users have a beacon that denotes the location of each user. The one that is the nearest from the beacon is selected from among multiple active media devices for servicing. In the experiments, four access points and one beacon was used to validate the proposed method. The advantage of the proposed method is that it enables us to apply active media technology to indoor environments. By recognizing user locations in indoor environments, several kinds of active media services become available.

Collaborative programming by demonstration in a virtual environment

Collaborative learning for a robot, a software agent, and a human subject in a virtual environment reduces time, labor, and cost compared to real-life approaches.

Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments

The Internet of Things (IoT) is the intelligent technologies and services that mutually communicate information between humans and devices or between Internet-based devices. In IoT environments, various device information is collected from the user for intelligent technologies and services that control the devices. Recently, wireless sensor networks based on IoT environments are being used in sectors as diverse as medicine, the military, and commerce. Specifically, sensor techniques that collect relevant area data via mini-sensors after distributing smart dust in inaccessible areas like forests or military zones have been embraced as the future of information technology. IoT environments that utilize smart dust are composed of the sensor nodes that detect data using wireless sensors and transmit the detected data to middle nodes. Currently, since the sensors used in these environments are composed of mini-hardware, they have limited memory, processing power, and energy, and a variety of research that aims to make the best use of these limited resources is progressing. This paper proposes a method to utilize these resources while considering energy efficiency, and suggests lightweight mutual verification and key exchange methods based on a hash function that has no restrictions on operation quantity, velocity, and storage space. This study verifies the security and energy efficiency of this method through security analysis and function evaluation, comparing with existing approaches. The proposed method has great value in its applicability as a lightweight security technology for IoT environments.

Beacon Distance Measurement Method in Indoor Ubiquitous Computing Environment

In the indoor ubiquitous computing environment where Global Positioning System (GPS) cannot be utilized, the approach to calculate the locations of Unmanned Aerial Vehicles (UAVs) is the core technique to control multiple UAVs. To calculate the locations of UAVs, the distance between Access Points (APs) and UAVs should be measured accurately given that the location of UAVs is obtained on the basis of the distance between APs and UAVs. In this paper, we propose a method to measure the distance between a single beacon and a single AP in an indoor ubiquitous computing environment. We assume that the beacon is attached to the bottom of a UAV. In the indoor experiment, while transferring a beacon, the distances between the beacon and an AP were measured and tuned. Therefore, the accumulated difference between the real beacon location and the calculated beacon location was reduced by 31.1 %.

Bayesian Probability-Based Hand Property Control Method

Deficiencies in low-priced motion recognition devices lead to diverse kinds of errors in recognizing palms and hands. To utilize lower-priced devices better, the recognition rate of the properties of hands should be improved. This chapter proposes a method that revises recognition errors in properties of hands. By calculating the Bayesian probability of the directions of a recognized palm, the directions were revised.