추현승 교수

In wireless sensor networks, density control is an important technique for prolonging a network's lifetime. To reduce the overall energy consumption, it is desirable to minimize the overlapping sensing area of the sensor nodes. In this paper, we study the problem of energy-efficient area coverage by the regular placement of sensors with adjustable sensing and communication ranges. We suggest a more accurate method to estimate efficiency than those currently used for coverage by sensors with adju

The Internet of Things (IoT) will include new devices specifically designed for IoT compatibility and systems that are already in place today and operate outside of IoT networks. However, the path to creating cloud networks of interconnected devices requires a means for devices that are not IP-based to connect without having to bear the cost of a full Ethernet or WiFi interface with the accompanying protocol stack. This can be achieved through the use of IoT gateways that bridge these devices to

Multicomputer systems achieve high performance by utilizing a number of computing nodes. Recently, by achieving significant reductions in communication delay, the three-dimensional (3D) torus has emerged as a new candidate interconnection topology for message-passing multicomputer systems. In this paper, we propose an efficient processor allocation scheme-scan search scheme-for the 3D torus based on a first-fit approach. The scan search scheme minimizes the average allocation time for an incomin

Abstract The development of large‐scale wireless sensor networks engenders many challenging problems. Examples of such problems include how to dynamically organize the sensor nodes into clusters and how to compress and route the sensing information to a remote base station. Sensed data in sensor systems reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. Noteworthy efficient clustering schemes and data compressing techniques proposed re

Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast

The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinfor

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studie

Data aggregation is one of the most essential operations in wireless sensor networks (WSNs), in which data from all sensor nodes is collected at a sink node. A lot of studies have been conducted to assure collision-free data delivery to the sink node, with the goal of minimizing aggregation delay. The minimum delay data aggregation problem gets more complex when recent WSNs have adopted the duty cycle scheme to conserve energy and to extend the network lifetimes. The reason is that the duty cycl

Sensor nodes transmit the sensed information to the sink through wireless sensor networks (WSNs). They have limited power, computational capacities and memory. Portable wireless devices are increasing in popularity. Mechanisms that allow information to be efficiently obtained through mobile WSNs are of significant interest. However, a mobile sink introduces many challenges to data dissemination in large WSNs. For example, it is important to efficiently identify the locations of mobile sinks and

Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another. Considering a single dataset keeps the variance, which results from differences, to a minimum. Having a robust FER system, which can work across several datasets, is thus highly desirable. The ai

Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containe