• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.80-87
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• 2006

In this paper, we analyze the performance of IEEE 802.11b WLAN communication between access point(AP) and mobile equipment(ME) in 2.4 GHz band with noise and interference factors. WLAN communication at in-vehicle environment is assumed as the communication between main vehicle controller and electronic device such as sensor, ECU (Electrical Control Unit) in vehicle on telematics field for implementing wireless vehicle control system. Received interference level from other system's mobile equipment in the same band and automobile noise from each part of vehicle can be the main factors that can cause increasing error rate of control signal. With these (actors, we focus on the Eb/No the BER performance of WLAN for analyzing the characteristic of interference factors by the measured bit error rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1390-1411
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• 2024

Underwater Wireless Sensors Networks (UWSNs) are deployed in remotely monitored environment such as water level monitoring, ocean current identification, oil detection, habitat monitoring and numerous military applications. Providing scalable and efficient routing is very challenging in UWSNs due to the harsh underwater environment. The biggest difficulties are the nodes inherent movement due to water current, long delay in data transmission, low bandwidth of the acoustic signal, high error rate and energy scarcity in battery powered nodes. Many routing protocols have been proposed to solve the aforementioned problems. There are three broad categories of routing protocols namely depth based, energy based and vector-based routing. Vector Based Forwarding protocols perform routing through virtual pipeline by defining their radius which give proper direction to packets communication. We proposed a routing protocol termed as Path-Oriented Energy Scaled Expanded Vector Based Forwarding (PESEVBF). PESEVBF takes into account all parameters; holding time, the source nodes packets routing path and void holes creation on the second hop; PESEVBF not only considers the packet upward advancement but also focus on density of the forwarded nodes in terms of number of potential forwarding and suppressed nodes for path selection. Node selection in resultant holding time is based on minimum Path Factor (PF) value. Moreover, the suppressed node will be selected for packet forwarding to avoid the void holes occurrences on the second hop. Performance of PESEVBF is compared with other routing protocols using matrices such as energy consumption, packet delivery ratio, packets dropping ratio and duplicate packets creation indicating considerable performance improvement.

• Korean Journal of Optics and Photonics
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• v.5 no.1
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• pp.166-172
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• 1994

The fiber optic receiver, ETRI APD-FET 1.0, is developed for the application of optical communication. This fiber optic receiver includes PD sub-module and pre-amplifier case. A single lens system is introduced for the PD sub-module. The sub-module consists of the avalenche photodiode(APD), GRIN rod lens, and a single mode fiber. The above components are enclosed into the stainless steel 304L housings. By bevelling the fiber end, the single mode fiber provides less than ~ 28 dB of optical return loss. The area of image focus is controlled by adjusting the length of spacer located in-between the fiber and the GRIN rod lens. The laser welding technique is applied to achieve the maximum coupling efficiency for the joining of each housing. In the pre-amplifier case, GaAs FET pre-amplifier workes for photocurrent amplification and the thermister is mounted to control the APD bias. The performance of ETRI APD-FET1.0 shows the sensitivity of - 30.3 dBm at $10^{-10}$ BER(bit error rate) and 2.5 Gbps optical random signal of $2^{23}-1$ word length. The fiber optic receiver is one of the essensial parts of the transmission module for B-ISDN. Also, the above optical packaging technology will be adapted for the developement of 10 Gbps transmission application 2.5 Gbps 5 Gbps

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.