The present study analyzed the elements of future-oriented development of Korean style-based software education for more effective operation centering on the software education that will be implemented as a regular curriculum subject from 2018 in South Korea. As for the analysis methods, unstructured data collected from the Web were managed in a big data store and trend analyses were conducted through the frequency counts of words utilizing the pretreatment technique. Based on the results of the present study, the proportion of positive elements has been higher thus far. However, the proportion of negative elements has been increasing over time. Therefore, it could be seen that for long-term development of software education, rather than coding centered education, the proportion of the learning of the ability to think computing would act as an important variable for development factors.
Nowadays, motion detection technology is an important field of investigation especially for those researchers whose field is human-computer interaction. Visual algorithms are generally getting complicated when the scale of information is huge. Under most of the situations, calculations need to be done rapidity. Vision sensor may not that appropriate. MEMS provides low dimensional data with stronger adaptability for various occasions. This paper represents a fitness device in which an acceleration sensor can capture users' movements. Experimental results confirm the feasibility of the fitness devices.
The emergence of Big Data due to spread of the 21st century digital economy can provide a clue to the solution of the problems in our society and economy. Especially, one of the areas where Big Data can be highly useful is the medical and health industry. The development of IT is leading a new era of innovation in the field of medicine as well. Nevertheless, the level of Big Data application in this field is still low as the unstructured data included in Big Data are difficult to search and gather their statistics so that there have been some limits in utilizing it widely. The merit of Big Data is that it can present a variety of meaningful results depending on the data collection and analysis methods. Thus, in this study, a Big Data has been created for text mining by using the crawling technique and analyzed with R Studio, followed by its visualization to devise an individualized health plan with different perspectives.
Horizontal damage zone is an important index to reflect the anti missile capability of ship-to-air missile. First, research the ship-to-air missile's horizontal damage zone problem. By the ship-to-air missile and target as far as the meeting point determine the boundary of horizontal damage zone. Then, analyzing the characteristics of different cooperative mode operation, and according to the different mode of cooperation gives the method of calculation of ship-to-air missile's horizontal damage zone. Last, giving an example of formation air defense operation, by Matlab simulation verifies the correctness of the conclusion.
In Sep. 2015, the Act on the Use of Numbers to Identify a Specific Individual in the Administrative Procedure was revised. It was decided to link personal numbers to deposit numbers of financial institutions. Currently, the Privacy Impact Assessment which is obliged to implement this law is required to implement safety control measures for the private sector. However, there is no system to conduct a risk assessment of the law. In the financial industry, which is a highly private sector of public nature, some privacy risk assessment is required because it has many individual numbers. In this paper, we propose a framework for privacy risk assessment on this law in the financial industry, using the privacy impact assessment prescribed as an international standard.
One of the most important cytological authorization methods to protect privacy is group signature method. Verifiers have limited information of the signers that gives the limited certainties that the signer is from the group while the specific identities of the signers are still concealed. In a group signature method, the openers can reveal the anonymous signers. However, the point that opener is always authorizable to check the signer’s identification without consent is useful to manage malice actions but is also doubtful for the signers because of the risk of the privacy exposure. This paper proposes a resolution for the signer’s anxiety. It tells that the signer has a right to create a token whenever they want to open their identity and only the opener with the token from the signer have access to the signer’s identification.
In the design of safety-critical embedded systems (SCES), the use of reliability measures is crucial to identify reliability-optimized and cost-optimized fault-tolerant mechanisms (FTM). The reliability improvement factor (RIF) was used in this study, which is a ratio of the probability of failure of the baseline system to that of the redundant system for a fixed mission time. We extend the analytical RIF into the simulation-based RIF (SRIF), as a relative measure of the reliability improvement for the FTM of SCES. We calculated the SRIF of the FTM by substituting the failure rate, which can be obtained from the statistical fault injection simulation by using co-simulation models and representative fault models. We use SRIF to compare the performance of FTMs and find the most reliable FTM. As a case study, we compare the SRIF of the dual-modular redundant (DMR) FTM with the triple-modular redundant (TMR) using ARM7 SystemC simulation models.
Early detection of rolling-element bearings faults is essential, and acoustic emission (AE) signals are actively utilized for monitoring bearing health condition. Most existing methods for fault diagnosis comprise two steps: feature extraction and fault classification. The convolutional neural network (CNN) is a powerful deep learning technique that can perform both feature extraction and classification procedures without the need to separate these tasks into different algorithms. However, most of the known CNN architectures are used for image recognition and require a 2-D image as an input parameter. To employ CNN to resolve the problem of rolling-element bearings fault diagnosis, in the present work, the raw 1-D AE signal is transformed into a 2-D kurtogram representation. Experimental results using eight types of various bearing conditions indicate that the proposed fault diagnosis approach utilizing the kurtogram representation of the original AE signal and CNN extracts discriminative features and achieve high classification accuracy.
Several standards for medical software require systematic development to ensure safety and performance. Core standards for medical software include IEC 60601-1, IEC 62034, and ISO 14971. And they present different activities. In this case, standards consist of contents related to development by referring to contents of one another. Therefore, it is difficult for a developer to identify reference-relationships with other standards for complying with one standard. For this, there are rules and studies that assist in the reference-association, but they do not provide the requirements at a view of developer. Therefore, we propose an integrated process to comply with the core standards. The proposed process is defined by analyzing the relationship between development process and so on. Then it includes the requirements and artifacts at each stage of the integrated process. This enables systematic development of medical software by providing the activities and requirements in terms of developer's view.
The limitation of a real-time video conferencing system is that it does not perfectly guarantee real-time transmission due to a delay in the network and buffering as well as ineffective communication of user information between systems. Studies are actively investigating the network infrastructure expansion and jitter delay in order to overcome this problem. However, there has not been much progress with respect to buffering delay. This paper suggests a Frame Rate Control Buffer (FRCB) management technique to solve problems that occur due to buffering delay. The FRCB is used to prevent buffer overflow and underflow by adopting two levels of buffer thresholds, Fast-play Threshold (FTH) and Slow-play Threshold (STH). It demonstrates superior performance compared to jitter buffer in conditions such as high CPU load, thus proving its suitability for high-quality real-time video conferencing.
Recently, deep learning based object recognition systems are very widely used in various fields, including surveillance systems. The accuracy of object recognition based on deep learning is better than other schemes. In this paper, we propose a wearable device for the blind by using deep learning based object recognition. Based on the implemented prototype and evaluation results, we confirmed the usefulness and effectiveness of the proposed wearable device.
Thermocline has always been the emphasis of marine research. In this paper, we propose a method to construct high resolution marine grid data sets on the basis of MLP. Data used in the article is from World Ocean Atlas 2013. The experiments show that high resolution data can calculate the depth, thickness and strength of thermocline precisely. The method is vital to thermocline gridding.
The convenience of small, cheep, and mobile communication devices such as laptops, cell phones, handheld devices, and mobiles sensor nodes, has popularized mobile ad hoc networks (MANETs). With the convenience, interconnection among these devices introduced new dimensions of challenges for the technology to be used for communication. Such challenges include wireless communication, mobility, and portability. Furthermore, the sparse behavior of nodes in turbulent areas, where connectivity is commonly not possible all the time, resulted in yet another exciting technology known as delay tolerant networks (DTNs). This work is related to the association of opportunistic techniques with different scenarios in which different opportunistic elements of relay nodes, e.g. message storage capacity, territory and velocity are classified according to its usefulness in a given scenario.
In Intelligent Transportation Systems (ITS), it is widely used to extract a fixed-size feature vector from raw traffic data for high-level traffic analysis. In several existing works, the statistical approach has been used for extracting feature vectors, which directly extracts features by averaging speed or travel time of each vehicle. However, we can achieve a better representation by taking advantage of state-of-the-art machine learning algorithms instead of the statistical approach. In this paper, we propose a two-phase framework named embed-and-aggregate framework for extracting features from raw traffic data, and a feature extraction algorithm (Traffic2Vec) based on our framework exploiting state-of-the-art machine learning algorithms such as deep learning. We also implement a traffic flow prediction system based on Traffic2Vec as a proof-of-concept. We conducted experiments to evaluate the applicability of the proposed algorithm, and show its superior performance in comparison with the prediction system based on the statistical feature extraction method.
In this paper, we propose a novel vision-based humanoid control method and visual tracking based on constant velocity (CV) model using the finite impulse response (FIR) filter. The proposed method has robust performance even if a sampling time or noise information is inaccurate. Furthermore, even when the movement of the detected ball or the ambient illuminance changes suddenly, the proposed method shows robust performance. The robust performance of the proposed method is verified through experimental results.
A definition of Load Loss Coefficient (LLC) is given in this study along with the power loss tracing algorithm. As LLC indicates the effect of load on power transmission loss, its calculation is performed based on the Bialek's power tracing method, where gross and net flows are being considered, to determine the power loss in a system during power transmission.
This work is to design an emotional analysis system using Deep Neural Network based on electroencephalogram data. The data are processed using high pass filtering and removing DC offset method in the proposed system. Then the preprocessed dataset is constructed to analysis the impact of input data placement on recognition performance. In the experiment, the happy and neutral dataset are used to measure the proposed approach performance. The result shows that learning data by stacking one row at a time is better than learning data matrix sequentially.
When it is necessary to adjust the power flow over a certain bus, the method that best uses given conditions to efficiently adjust power flow is essential. There are several ways to control the power flow on transmission lines by adjusting the power generations but the method that controls the flow by rescheduling the generation is not adequate for use as it is not easy to carry out power flow adjustment on a particular line or it is not clear which power generator's power generation capacity should be adjusted as the active power has little regional constraints. Thus, a method that can cope with these problems is required. Therefore, a method that trace the origin of power flow over a particular power line by using the power flow tracing technique to control power generation level and eventually adjust power flow has been proposed in this study.
This paper mainly focuses on the consensus problems under fuzzy environment, in which experts' original opinions take the form of intuitionistic fuzzy numbers. Based on the objective of minimizing the total consensus cost, we develop a novel intuitionistic minimum-cost consensus model (MCCM) in order to evaluate the deviation between individual opinions and group opinion. The proposed model can not only yield optimal adjusted individual opinions and consensus opinion, but also can explore index of each expert's risk-bearing attitude. Additionally, some intuitionistic consensus models under WA operator and OWA operators are presented. With the help of multi-objective programming theory, linear-programming-based approaches are put forward to solve these consensus models. Finally, a numerical example is implemented to demonstrate the accuracy and effectiveness of the proposed models.
