Yeongjoon Kim

EEP-MAC: An energy efficient priority-based MAC protocol for Wireless Sensor Networks

In this paper, we present an energy efficient priority-based MAC (EEP-MAC) protocol for energy harvesting Wireless Sensor Networks (WSNs). To provide priority-based data transmission the EEP-MAC uses the modified IEEE 802.15.4 beacon frame. A receiver node periodically wakes up, receives sender beacon frames from its neighbors, and broadcasts a beacon frame containing the selected senders address. A receiver node selects sender node according to senders data priority. Under EEP-MAC protocol, no hidden terminal problem can happen. We show the performance of EEP-MAC through simulations and analytical analysis, and compare it against the previous MACs. Results have shown that our protocol outperforms other MAC in terms of energy consumption, higher priority packet delivery delay.

Distributed channel routing using genetic algorithm

Known as an NP-Complete problem, the channel routing problem is very important in the automatic layout design of VLSI circuit and printed circuit boards. In this paper, a distributed genetic algorithm for this channel routing problem is presented in MPI environments. Each node executes genetic operations to own sub-population and communicates synchronously with other nodes to form the global population. The experimental results show that the proposed algorithm maintains the convergence properties of sequential genetic algorithm.

Distributed simulated annealing for composite stock cutting problem

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we propose distributed simulated annealing for this composite stock cutting problem. The new cost error tolerant scheme relaxes synchronization and does move generations asynchronously in a dynamically changed stream length to keep the convergence property of the sequential annealing. This paper also proposes the efficient data structures for pattern related information.

Asynchronous distributed genetic algorithm for optimal channel routing

This paper presents a distributed genetic algorithm for the channel routing problem in MPI environments. This system is implemented on a network of personal computers running Linux operating system connected via 10Mbps Ethernet. Each slave processor generates its own sub-population using genetic operations and communicates with the master processor in an asynchronous manner to form the global population. The experimental results show that the proposed algorithm maintains the convergence properties of sequential genetic algorithm while it achieves linear speedup as the nets of the channel routing and the number of computing processors increase.

A Multimedia Authoring and Virtual Collaboration System Supporting Multi-Conferences for E-Learning

We are proposing the multimedia presentation authoring and virtual collaboration system that produces multimedia contents and enables multiple users who are dispersed in locally and timely to collaborate by the Internet. The proposed system consists of two parts—multimedia presentation authoring and recordable virtual collaboration tools. The authoring tool makes it possible to create and edit multimedia presentations that integrate diverse media types including images, video, sound, and texts for e-learning. Media objects are synchronized with the temporal and spatial information using SMIL defined by W3C. The collaboration tool categorizes users by the interested conference and any users can create new topics or join the existing topics by validating user’s access right freely in a conference they belong. Users can participate in more than one topic simultaneously, so they can inquire and get a valuable knowledge on-line in one topic and participate in the other topic more informed and intelligent. The produced multimedia presentation may be provided to the users through the conference before the discussion begins. Users can use text along with associated symbols such as arrows and polygons over the presented images during the discussion. Users’ opinions along with symbols are recorded to XML database.

A Web-Based Virtual Collaboration System in Multiple Conferences

We are proposing the web-based virtual collaboration system where multiple users are collaborated dispersed in locally and timely by the Internet. The proposed system categorizes users by the interested conference and each user is restricted to access the system resources by the access control. Any users can crate new topics or join the existing topics by validating user’s access right freely in a conference they belong. Users can participate in more than one topic simultaneously, so they can inquire and get a valuable knowledge on-line in one topic and participate in the other topic more informed and intelligently. They can also save time by attending multiple topics at the same time. Conferences are performed by using power point slides and associated spatial data elements from the participants. Associated spatial data elements can be symbols such as arrows or polygons along with text. The content of each conference – spatial data elements from the participants with respect to certain topic – can be recorded into an XML file and archived for future reference.

A Multi-classifier System Using Mean Field Genetic Algorithm

This paper presents an approach for building a multi-classifier system in Mean Field Genetic Algorithm (MGA) based inductive learning environments. Several base classifiers are combined with a meta-classifier that learns the bias of base classifiers so that it can draw a decision by combining predictions made by base classifiers. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA.

Learning the bias of a classifier in a GA-Based inductive learning environment

We have explored a meta-learning approach to improve the prediction accuracy of a classification system. In the meta-learning approach, a meta-classifier that learns the bias of a classifier is obtained so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classification system. The paper discusses our meta-learning approach in details and presents some empirical results that show the improvement we can achieve with the meta-learning approach in a GA-based inductive learning environment.

The analysis of hardware supported cache lock mechanism without retry

A lock mechanism is essential for synchronization on the multiprocessor systems. This paper proposes the new locking protocol, called WPV (Waiting Processor Variable) lock mechanism. It uses the cache state lock mechanism and has only one lock-read bus traffic. This paper also derives the analytical model of WPV lock mechanism as well as conventional memory and cache queuing lock mechanisms. The simulation results on the WPV lock mechanism show that access time is reduced comparing with the memory and queuing lock mechanism as the number of processors increases.

A Novel Hybrid Approach of Mean Field Annealing and Genetic Algorithm for Load Balancing Problem

In this paper, we introduce a new solution for the load balancing problem, which is an important issue in parallel processing. Our novel load balancing technique called MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). SGA uses the Metropolis criteria for state transition as in simulated annealing to keep the convergence property in MFA. The MGA combines the benefit of both the rapid convergence property of MFA and various and effective genetic operations of SGA. We compare the proposed MGA with MFA and GA. Our experimental results show that our new technique improves performance in terms of communication cost, load imbalance and maximum execution time.