• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.20-28
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• 2007

Zigbee sensor network base on IEEE802.15.4 has local address of 2 byte on transmit packet data which is pick up the address for each sensor node. Sensor network is requested low power, low cost, many nodes at hues physical area. There for Zigbee is very good solution supporting for next Ubiquitous generation but the Zigbee sensor network has address allocation problem of each sensor node. Is established standard from Zigbee Alliance, to the address allocation method uses Cskip algorithm. The Cskip algorithm use the hazard which allocates an address must blow Hop of the maximum modification and child node number. There is to address allocation and from theoretically it will be able to compose a personal 65536 sensor nodes only actual with concept or space, only 500 degree will be able to compose expansion or the low Zigbee network. We proposed an address allocation method using coordinate value for Zigbee sensor network.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.88-88
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• 2017

The study was conducted to analyze the growth characteristics comparison per planting density on the waxy corn early-planting culture for the paddy field in the southern province of south Korea. The cultivation period of early-planting culture for the paddy farming of the waxy corn are sown on February 15, 2016years, transplanting March 15 and harvest June 20. And it grew 126 days. The weather change according to the cultivation period of unheated plastic house early-planting culture, it was average temperature $14.6^{\circ}C$ and humidity 62.5%. And the temperature was 5.6 degrees Celsius warmer compared with the outside temperature and the humidity was 0.7 percent higher tendency. At the growth per planting density of waxy corn, culm length was average 224cm, the more it is high density culture the more was high trend. Stem diameter and ear length the more it is high density culture the more was lowed trend. The node number of $60{\times}20Cm$ was 12 nodes, fruit seting 5.7 nodes, tasseling number 94 days and silking number 96 days. In the ear characteristics per planting density, the size of ear length, seed setting length, ear width and ear weight the more planting density is high the more lowed that trend. The commodity percentage of planting density $60{\times}35Cm$ was the highest among other treatment as 69.1%. But, marketable yield was the highest planting density of $60{\times}20Cm$ as 4,543 ears/10a, and appeared in order $60{\times}25Cm$ 95%> $60{\times}30Cm$ 93%> $60{\times}35Cm$ 92%. The planting density on the waxy corn early-planting culture for the paddy farming in the southern province, the planting density analyzed to be effective planting of over 25% than normal season culture.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.8-16
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• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.163-171
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• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• The Journal of Korean Physical Therapy
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• v.15 no.2
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• pp.75-84
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• 2003

The purpose of this study was to review of mechanism and application methodology about mental practice. The mental practice is symbolic rehearsal of physical activity in the absence of any gross muscular movements. Human have the ability to generate mental correlates of perceptual and motor events without any triggering external stimulus, a function known as imagery, Practice produces both internal and external sensory consequences which are thought to be essential for learning to occur, It is for this reason that mental practice, rehearsal of skill in imagination rather than by overt physical activity, has intrigued theorists, especially those interested in cognitive process. Several studies in sport psychology have shown that mental practice can be effective in optimizing the execution of movements in athletes and help novice learner in the incremental acquisition of new skilled behaviors. There are many theories of mental practice for explaining the positive effect In skill learning and performance. Most tenable theories are symbolic learning theory, psyconeuromuscular theory, Paivio's theory, regional cerebral blood flow theory, motivation theory, modeling theory, mental and muscle movement nodes theory, insight theory, selective attention theory, and attention-arousal set theory etc.. The factors for influencing to effects of mental practice are application form, application period, time for length of the mental practice, number of repetition, existence of physical practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• Journal of Gastric Cancer
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• v.18 no.2
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• pp.152-160
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• 2018

Purpose: Totally laparoscopic gastrectomy (TLG) for advanced gastric cancer (AGC) is a technically and oncologically challenging procedure for surgeons. This study aimed to compare the oncologic feasibility and technical safety of TLG for AGC versus early gastric cancer (EGC). Materials and Methods: Between 2011 and 2016, 535 patients (EGC, 375; AGC, 160) underwent curative TLG for gastric cancer. Clinicopathologic characteristics and surgical outcomes of both patient groups were analyzed and compared. Results: Patients with AGC required a longer operation time and experienced more intraoperative blood loss than those with EGC did. However, patients from both the AGC and EGC groups demonstrated similar short-term surgical outcomes such as postoperative morbidity (14.4% vs. 13.3%, P=0.626), mortality (0% vs. 0.5%, P=0.879), time-to-first oral intake (2.7 days for both groups, P=0.830), and postoperative hospital stay (10.2 days vs. 10.1 days, P=0.886). D2 lymph node dissection could be achieved in the AGC group (95%), with an adequate number of lymph nodes being dissected ($36.0{\pm}14.9$). In the AGC group, the 3-year overall and disease-free survival rates were 80.5% and 73.7%, respectively. Conclusions: TLG is as safe and effective for AGC as it is for EGC.

• Journal of Environmental Science International
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• v.29 no.4
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• pp.423-434
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• 2020

The effects of amino acid and/or urea liquid fertilizer application on the growth and phytochemicals of Perilla leaves were summarized as follows; The fresh weight of the perilla leaves was in the order of CF, CL, KLF, and control, and 39.7 g, 37.4 g, 36.5 g and 32.3 g were measured. The plant height increased by 71.6 cm in the CF treatment than in the control(54.6 cm). The number of nodes was 14.3 node in CF treatment and 19% more than control(12 node). The vitamin C content tends to be increased by fertilizing the amino acid solution in the perilla leaf. The components of polyvalent unsaturation of n-6 origin were measured in CF treatment, KLF treatment, and control in 10.19 mg, 10.18 mg, and 9.38 mg per 100 g, respectively. Glutamic acid, aspartic acid, leucine, arginine, alanine and lysin were contained in perilla leaf amino acid. Glutaminic acid content was found to be 455.1 mg, 495.6 mg, and 478.8 mg in the control, KLF and CF treatment per 100 g, respectively. Effective nutrition management using amino acid fertilizer optimizes crop yield and profitability, it is important to reduce the negative environmental risks of using fertilizer.

• Steel and Composite Structures
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• v.51 no.4
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.