Computer Engineering

In general, routing algorithms that were applied to ad-hoc networks are not suitable for the environment with many nodes over several thousands. To solve this problem, hierarchical management to these nodes and clustering-based protocols for the stable maintenance of topology are used. In this paper, we propose the clustering-based modified LEACH protocol that can applied to an environment which moves around metal containers within communication nodes. In proposed protocol, we implemented a module for detecting the movement of nodes on the clustering-based LEACH protocol and improved the defect of LEACH in an environment with movable nodes. And we showed the possibility of the effective communication by adjusting the configuration method of multi-hop. We also compared the proposed protocol with LEACH in four points of view, which are a gradual network composition time, a reconfiguration time of a topology, a success ratio of communication on an containers environment, and routing overheads. And to conclude, we verified that the proposed protocol is better than original LEACH protocol in the metal containers environment within communication of nodes.

Currently a number of managers exist to manage heterogeneous networks, in this situation, the NETCONF protocol for efficient network management has been proposed as a new protocol. However, the standard NETCONF protocol stack continuous improvement since the establishment but in four layers still have some problems. Especially in situations where there are multiple administrators, problems are more highlighted in operation layer. In this paper, we focus on these issues and the Operation layer has improved the efficiency and flexibility of operations among NETCONF four layers. Additionally, for the inefficiency of updates improved the device settings based on improved operation techniques. In addition, standard protocol NETCONF did not proposed content layer data structure and we propose standard technique of content layer that can generate configuration structure of devices. Improved the three techniques are applied appropriately to the NETCONF, the proposed method and the existing NETCONF was performed experiment to compare with experimental four factors. Compare key factor are four kind as maintaining the probability of network function, the reaction performance about command, the number of control packets, performance of data creation in content layer. Such factors after performing the experiment, the proposed method in this paper is superior to the existing NETCONF and there was confirmed by analysis Experimental results.

Recently, there have been some researches in the equipment fault detection based on shock and vibration information. Most research of them is based on shock and vibration monitoring to determine the equipment fault or not. Different with engine fault detection based on shock and vibration information we focus on detection of engine for boat and system control. First, it use the duplicated-checking method for shock and vibration information to determine the engine fault or not. If there is a fault happened, we use the integral to determine the error engine shock wave width and detect the fault area. On the other hand, we use the engine trend analysis and standard of safety engine to implement the shock and vibration information database. Our simulation results show that the probability of engine fault determination is 98% and the probability of engine fault detection is 72%

Modern people cannot manage their mental health properly because of busy work and stress. Especially, panic disorder, which became an issue for celebrities these days, also affects modern people. Among panic disorder symptoms, there is agoraphobia dislike of a specific place or situation. To treat these symptoms, what is called exposure training can be used. The disadvantages of exposure training are cost, manpower and the time it takes. n this paper, we developed an app for measuring the anxiety of people with generalized anxiety disorder and panic disorder, and developed a training system using VR technology to solve problems of exposure training. The two developed systems are expected to be helpful in treating patients with panic disorder.

NETCONF was originally proposed as a protocol for updating and managing configuration data on complex heterogeneous network equipment that forms part of a larger current network. There is particularly the problem of processing efficiency in the Operation and Content layers. Currently, NETCONF uses YANG to generate an XML document as a data model. The unique operations of NETCONF are used to change the configuration of the equipment. However, when there are multiple managers, the standard NETCONF exhibits relatively low data modeling flexibility, and this decreases the query processing throughput. In this paper, we propose the use of a YANG XML document that describes a data block generation method based on the dependencies of equipment configuration data. The proposed technique can be used to generate a logical structure of the equipment configuration data, which may be stored and grouped as a set of independent equipment data in the physical memory. Hence, when multiple network adm...

NETCONF was originally proposed as a protocol for updating and managing configuration data on complex heterogeneous network equipment that forms part of a larger current network. There is particularly the problem of processing efficiency in the Operation and Content layers. Currently, NETCONF uses YANG to generate an XML document as a data model. The unique operations of NETCONF are used to change the configuration of the equipment. However, when there are multiple managers, the standard NETCONF exhibits relatively low data modeling flexibility, and this decreases the query processing throughput. In this paper, we propose the use of a YANG XML document that describes a data block generation method based on the dependencies of equipment configuration data. The proposed technique can be used to generate a logical structure of the equipment configuration data, which may be stored and grouped as a set of independent equipment data in the physical memory. Hence, when multiple network administrators modify different data blocks, the processes can be performed concurrently. We performed experiment for various factors and confirmed that our improve NETCONF is outperform for existing method or protocol.

Modern networks consisting of various heterogeneous equipment are often installed in a distributed manner. Thus the NETCONF standard was established to manage networks centrally and efficiently. In this paper, we present a method that integrates each NETCONF layer into a single system based on the results of previous studies. In the RPC Layer, an asynchronous communication channel and parallel processes are possible using multi-threading. In the Operation Layer, operational efficiency is increased by using a data group with dependencies between the equipment configuration data and by improving the data structure, enabling efficiently processing of XML queries even with multiple managers. The data modeling techniques and grouping methods in the Content Layer are presented in detail for interoperability between the Operation Layer and the Content Layer. Finally, the GUI program was implemented and its implementation is reported. We performed an experiment comparing the improved NETCONF with the standard NETCONF to measure factors, such as query processing ratio, query processing speed, and CPU utilization. The improved NETCONF demonstrated excellent query processing ratio and query processing speed, whereas the standard NETCONF had excellent CPU utilization.

Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.