• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.68-73
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• 2018

In recent years, the utilization of the WSN have been rapid. Energy consumption of these networks must be as low as possible. LEACH algorithm is one of the clustering technique. We modify the traditional LEACH algorithm in such way that it will be capable to self-organize large number of nodes and for saving communication resources such as processing time and initiation time. The efficiency of the network highly depends on how the algorithm divides cluster area and selects cluster head. The proposed algorithm can be evaluated through the extensive simulation the result we obtained shows that the life time of a network is increased when energy load is distributed equally among the sensor.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• ETRI Journal
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• v.45 no.4
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• pp.570-580
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• 2023

Sensor nodes are the most significant part of a wireless sensor network that offers a powerful combination of sensing, processing, and communication. One major challenge while designing a sensor node is power consumption, as sensor nodes are generally battery-operated. In this study, we proposed the design of a low-power, long range-based wireless sensor node with flexibility, a compact size, and energy efficiency. Furthermore, we improved power performance by adopting an efficient hardware design and proper component selection. The Nano Power Timer Integrated Circuit is used for power management, as it consumes nanoamps of current, resulting in improved battery life. The proposed design achieves an off-time current of 38.17309 nA, which is tiny compared with the design discussed in the existing literature. Battery life is estimated for spreading factors (SFs), ranging from SF7 to SF12. The achieved battery life is 2.54 years for SF12 and 3.94 years for SF7. We present the analysis of current consumption and battery life. Sensor data, received signal strength indicator, and signal-to-noise ratio are visualized using the ThingSpeak network.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.123-126
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• 2016

Energy efficiency in heterogeneous network is considered as one of the main issues when deploying the wireless sensor network. In heterogeneous network, the random distribution of initial energy at each node could lead to an instability of the network. Therefore, a resonable policy must be established in order to maintain the fairness in energy consumption and extend the working time of each node in the network. In this paper, we evaluate the performance of the distributed clustering protocol (DCP) in heterogeneous network on different scenarios. Simulation results are compared with the results of a LEACH protocol in a heterogeneous network. In addition, the performance of system in heterogeneous network are also compared with the homogeneous network to illustrate the effect of imbalance in the initial energy on the life time of each node in the system. The result illustrates that the DCP protocol demonstrates better performance than LEACH protocol in both the heterogeneous and the homogeneous networks.

• International Journal of Railway
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• v.6 no.1
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• pp.13-25
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• 2013

Railpad prevents damage of the tie and ballast by reducing the impact and high frequency vibration, which occurs when a vehicle load transfers to a tie. But elasticity of the railpad can decrease under vehicle load and over usable period. If that happens, railpad will become stiffer. Increase in stiffness of the railpad also translates into a rise in track maintenance cost because it accelerates the damage of the track. In this study, accelerated heat ageing test was performed to predict an expectable lifetime of the railpad. As a result, it was predicted to be about sixteen years at $25^{\circ}C$ that life time of railpad using NR rubber from Arrhenius relationship. Also, it was predicted to be about thirty-two days at $100^{\circ}C$. At this time, a standard rate of thickness change is approximately within 12%.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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• pp.125-129
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• 2004

For the diagnosis of compressor abnormal condition in air-conditioner, AE signal which is derived from wear condition, compressed air and assembly error is analyzed experimentally. The burst and continuous type AE signal occurred by metal contact and compressed air and AE raw signal of compressors were directly acquired in production line. After extracting samples according to waveforms, Early Life Test(ELT) is conducted and classified to normal and abnormal waveform. The efficient parameters of waveform pattern are investigated in time and frequency domain and the diagnosis algorithm of air-conditioner by Neural Network estimation is suggested.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.119-130
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• 2023

Worldwide organizations use the benefits offered by Cloud Computing (CC) to store data, software and programs. While running hugely complicated and sophisticated software on cloud requires more energy that causes global warming and affects environment. Most of the time energy consumption is wasted and it is required to explore opportunities to reduce emission of carbon in CC environment to save energy. Many improvements can be done in regard to energy efficiency from the software perspective by considering and paying attention on the energy consumption aspects of software's that run on cloud infrastructure. The aim of the current research is to propose a framework with an additional phase called parameterized development phase to be incorporated along with the traditional Software Development Life cycle (SDLC) where the developers need to consider the suggested techniques during software implementation to utilize low energy for running software on the cloud and contribute in green computing. Experiments have been carried out and the results prove that the suggested techniques and methods has enabled in achieving energy consumption.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.167-172
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• 2017

Mobile nodes are used for prolonging the life-time of the entire wireless sensor networks and many studies that use drones to collected data have been actively conducted with the development of drone related technology. In case of associating a drone and tactical wireless sensor networks, real-time feature and efficiency are improved. The previous studies so focus on reducing drone's flight distance that the energy consumption of sensor nodes is unbalanced. This unbalanced energy consumption accelerates the network partition and increases drone's flight distance. In this paper, we proposed a new selection scheme considered drone's flight distance and nodes' life-time to solve this problem when rendezvous nodes that collect data from their cluster and directly communicate with a drone are selected.

• Journal of KIISE
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• v.41 no.11
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• pp.958-966
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• 2014

Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.