• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.258-269
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• 2020

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1141-1144
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• 1998

A new design methodology for the shortchannel CMOS IC-package is presented. It is developed by representing the package inductance with an effective lumpedinductance. The worst case maximum-simultaneous-switching noise (SSN) and gate propagation delay due to the package are modeled in terms of driver geometry, the maximum number of simultaneous switching drivers, and the effective inductance. The SSN variations according to load capacitances are investigated with this model. The package design techniques based on the proposed guidelines are verified by performing HSPICE simulations with the $0.35\mu\textrm{m}$ CMOS model parameters.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 1999.11a
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• pp.33-36
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• 1999

In this paper, we proposed a simultaneous switching noise(SSN) reduction technique in muti-layer beards(MLB) for high-speed digital applications and analyzed them using the Finite Difference Time Domain(FDTD) method. The new method by conductive dielectric substrates reduces SSN couplings and resonances, significantly, which cause series malfunctions in the modem high-speed digital applications.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.44-47
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• 2003

The split ground structure for microstrip structure can be used to protect analog/RF signal from SSN interference of digital circuits on PCB with common ground. However, the split ground structure gives rise to undesired emissions that may interfere with nearby circuitry due to the ground discontinuity. In this paper, we have proposed the modified structure, Dumbbell shaped SGS and the method to reduce the radiation by adding the lumped resistor on the proposed Dumbbell shaped SGS.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.35-39
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• 2010

Generally, ceramic material is very difficult to machine due to high strength and hardness. However, ceramic material can be machined at high temperature by plastic flow as metallic material due to the deterioration of the grain boundary glassy phase. Recently, a new method was developed to execute cutting process with CBN cutting tool by local heating of surface with laser. There are various parameters in LAM because it is a complex process with laser treatment and machining. During laser assisted machining, high power results in reducing of cutting force and increasing tool life, but excessive power brings oxidation of the surface. The effect of laser power, feed rate, cutting depth and etc. were investigated on the life of cutting tool. Chips were observed to find out suitable machining conditions. Chips of SSN had more flow-types than HIPSN. It means SSN is easier to machining. The life of cutting tool was increased with increasing laser power and decreasing feed rate and cutting depth.

• Korean Journal of Medicinal Crop Science
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• v.25 no.4
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• pp.238-243
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• 2017

Background: The production method of ginseng seedlings for ginseng cultivation is very important to ensure healthy rooting system as well as high quality, and yield of the resultant plants. This study was carried out to compare the growth characteristics of 2-year-old ginseng plants that were produced from seedlings grown in self soil nursery (SSN), nursery soil (NS) or hydroponic culture (HC). Methods and Results: The shading prop used was composed of four-layered 4 polyethylene (blue 3 + black 1) shade screen. The management of main field was done by inserting oil cake (1,200 kg/10 a) and then allowing Sudan grass to grow for a year. Seedling transplantation was carried out on April 6. Root growth was measured on October 25. Root weight was observed to be excellent at 6.0 g, following SSN transplantation. Root length was 21.2 cm for HC seedlings, but these plants had a physiological disorder (i.e., rusty root), in 83.5% plants of this treatment. The ratio of PD/PT (protopanaxadiol saponins / protopanaxatriol saponins) was higher in NS seedlings. Plant analysis revealed that Fe content was lower in HC seedlings with high rustiness. The growth of 2-years-old ginseng was different following these varying seedling cultivation methods, but seedlings from NS were not different from those grown in SSN. Conclusions: For the propagation of 2-year-old ginseng plants, NS seedlings may be a good substitute for SSN seedlings.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.

• ETRI Journal
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• v.38 no.5
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• pp.952-961
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• 2016

In this paper, we study a spectrum sharing network (SSN) where a spectrum sharing device (SSD) coexists with multiple wireless communication systems (WCSs) in the same channel. The SSD can operate with either a duty cycle (DC) channel access mechanism or a listen-before-talk (LBT) channel access mechanism, whereas WCSs operate with an LBT mechanism. An opportunistic channel selection scheme for the SSD in the SSN is first proposed to minimize the outage probability. The optimal data transmission time for the DC-based SSD is derived to further improve the outage probability. We also derive the exact and closed-form outage probability of the proposed channel selection in the SSN by assuming that the number of WCSs operating in each channel is uniformly distributed. The simulation results show that the proposed channel selection scheme outperforms other channel selection schemes. It was also observed that a DC-based SSD with an optimal data transmission time provides a better outage performance than an LBT-based SSD. As the number of available channels increases, the channel selection scheme plays an important role in minimizing the outage probability of the SSNs.

• ETRI Journal
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• v.45 no.6
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• pp.1079-1089
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• 2023

Stroke is one of the leading causes of long-term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short-term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand-to-mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke-affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real-time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest-Mounted Accelerometer dataset, and 10% higher than another real-world dataset.

• Nuclear industry
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• v.28 no.4 s.302
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• pp.69-70
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• 2008