As the capacity of NAND flash module increases, the amount of RAM increases for caching and maintaining the FTL mapping information. In order to reduce the amount of mapping information managed in the RAM, a demand-based address mapping method stores the entire mapping information in the flash and some valid mapping information in the form of cache in the RAM so that the RAM can be used efficiently. However, when cache miss occurs, it is necessary to read the mapping information recorded in the flash, so overhead occurs to translate the address. If the RAM space is not enough, the cache hit ratio decreases, resulting in greater overhead. In this paper, we propose a method using two tables called TPMT(Translation Page Mapping Table) and SMT(Segmented Translation Page Mapping Table) to utilize both temporal locality and spatial locality more efficiently. A performance evaluation shows that this method can improve the cache hit ratio by up to 30% and reduces the extra translation operations by up to 72%, compared to the TPM scheme.

In this paper, we propose a development method of portable Automatic Test Equipment based on PXIe platform. Legacy VME form factor structured test equipment has limited reuse and expansion of modules due to unapplied bus specification. In particular, these limitations can cause development periods and costs to increase, and the reliability of environmental conditions is lacking due to non-standard modules. The test equipment of the proposed PXIe platform can use diverse COTS modules to shorten the development period and minimize the instability between developments. The PXIe development module works with standard Xilinx FPGAs, PXIe Windows device drivers, and applications on standard PXIe buses. The use of standard bus and COTS modules increases scalability and reusability, enabling rapid development and excellent maintenance. Through the test, we show the proposed test equipments can be implemented efficiently between the development processes and proved their reliability through function tests and environmental tests.

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

In this paper, we propose three compensation methods to solve problems in high-resolution airborne infrared camera and to improve long-range target information acquisition performance. First, image motion and temporal noise reduction technique which is caused by atmospheric turbulence. Second, thermal blurring image correction technique by imperfect performance of NUC(Non Uniformity Correction) or raising the internal temperature of the camera. Finally, DRC(Dynamic Range Compression) and flicker removing technique of 14bits HDR(High Dynamic Range) infrared image. Through this study, we designed techniques to improve the acquisition performance of long-range target information of high-resolution airborne infrared camera, and compared and analyzed the performance improvement result with implemented images.

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

Recently, there has been an increasing need for the sharing of microdata containing information regarding an individual entity. As microdata usually contains sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, to avoid the privacy problems that occur through the release of microdata for public use, extensive studies have been conducted in the area of privacy-preserving data publishing (PPDP). The k-anonymity algorithm, which is the most popular method, guarantees that, for each record, there are at least k-1 other records included in the released data that have the same values for a set of quasi-identifier attributes. Given an original table, the corresponding k-anonymous table is obtained by generalizing each record in the table into an indistinguishable group, called the equivalent class, by replacing the specific values of the quasi-identifier attributes with more general values. However, query processing over the anonymized data is a very challenging task, due to generalized attribute values. In particular, the problem becomes more challenging with an equi-join query (which is the most common type of query in data analysis tasks) over k-anonymous tables, since with the generalized attribute values, it is hard to determine whether two records can be joinable. Thus, to address this challenge, in this paper, we develop a novel scheme that is able to effectively perform an equi-join between k-anonymous tables. The experiment results show that, through the proposed method, significant gains in accuracy over using a naive scheme can be achieved.

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

Correct prediction of emotion is essential for developing advanced health devices. For this purpose, neural network has been successfully used. However, interpretation of how a certain emotion is predicted through the emotion prediction neural network is very tough. When interpreting mechanism about how emotion is predicted by using the emotion prediction neural network can be developed, such mechanism can be effectively embedded into highly advanced health-care devices. In this sense, this study proposes a novel approach to interpreting how the emotion prediction neural network yields emotion. Our proposed mechanism is based on HRV (heart rate variability) measurements, which is based on calculating physiological data out of ECG (electrocardiogram) measurements. Experiment dataset with 23 qualified participants were used to obtain the seven HRV measurement such as Mean RR, SDNN, RMSSD, VLF, LF, HF, LF/HF. Then emotion prediction neural network was modelled by using the HRV dataset. By applying the proposed mechanism, a set of explicit mathematical functions could be derived, which are clearly and explicitly interpretable. The proposed mechanism was compared with conventional neural network to show validity.

In this paper, we propose a thorough solution for adjusting virtual sound field with different kinds of devices and software in preliminary procedure and late stage of music processing. The basic process of music production includes composing, arranging and recording at pre-production stage as well as sound mixing and mastering at post-production stage. At the initial stage of music creation, it should be checked whether the design of virtual sound field, the choice of the tone and the instrument used in the arrangement match the virtual sound field required for the final work. In later recording, mixing and mastering, elaborate adjustments should be done to the virtual sound field. This study also analyzed how to apply the parameter of the effectors to the design and adjustment of the virtual sound field, making it the source of our creation.

In this paper, we propose a methodology of using time-lag, make it serve the sound field, in order to smoothen the music production and reduce conflicts. The importance of music production in today's music industry chain is becoming more and more apparent. In the process of music production, the creators pay more attention to the design and adjustment of virtual sound field, especially the late mixing and production. In the process, as a commonly used tool for the adjustment of sound field, "time-lapse" plays a decisive role.

A wireless image sensor node collecting image data for environmental monitoring or surveillance requires a large amount of energy to transmit the huge amount of video data. Even though solar energy can be used to overcome the energy constraint, since the collected energy is also limited, an efficient energy management scheme for transmitting a large amount of video data is needed. In this paper, we propose a method to reduce the number of blackout nodes and increase the amount of gathered data by selecting an appropriate video coding method according to the energy condition of the node in a solar-powered wireless video sensor network. This scheme allocates the amount of energy that can be used over time in order to seamlessly collect data regardless of night or day, and selects a high compression coding method when the allocated energy is large and a low compression coding when the quota is low. Thereby, it reduces the blackout of the relay node and increases the amount of data obtained at the sink node by allowing the data to be transmitted continuously. Also, if the energy is lower than operating normaly, the frame rate is adjusted to prevent the energy exhaustion of nodes. Simulation results show that the proposed scheme suppresses the energy exhaustion of the relay node and collects more data than other schemes.

This study was performed to investigate the changes in turnover intention according to the level of occupational stress and to find the mediating factor that reducing the turnover intention among fire officer. To compare change of turnover intention according to the degree of occupational stress, statistical analyses were done by using the logistic regression model. In logistic regression analysis, the possibility of high turnover intention in a group with high occupational stress was hjgher by 4.11 times than a group with low occupational stress. The results of analyzing the degree of change in turnover intention after applying the mediating parameters(physical condition, emotional labor, burn out), turnover intention decreased by about 50.6%(from 4.11 times to 2.03 times) at the high level of occupational stress. As a result, it was found that the occupational stress experienced by the fire-officers had a positive effect on the turnover intention. In order to reduce the turnover intention due to the occupational stress of the fire-officers, it is necessary to manage factors such as work environmental factors(emotional labor, burn out) and individual factor(physical condition).

This is a descriptive study to investigate the relationship between MBTI personality type, clinical practice stress, and resilience in nursing college students. The participants of this study were 75 college students of Gangwon-do nursing college. The data were analyzed by t-test, ANOVA, $Scheff{\acute{e}}$ test and Pearson correlation coefficient using SPSS win18.0. Thirteen personality types were observed in this study participants. The NT types was the highest with a mean of 4.15 in clinical practice stress according to 4 functions of MBTI and the ST types was the highest with a mean of 3.32 in resilience according to 4 fuctions of MBTI. The NT type was the highest with a mean of 4.15 in clinical practice stress according to 4 temperaments of MBTI and the SJ type was the highest with a mean of 3.27 in resilience according to 4 temperaments of MBTI. Significant differences in clinical practice stress were observed according to 4 functions(F=27.117, p<.001) and 4 temperaments(F=27.310, p<.001) of MBTI. Significant differences in resilience were observed according to 4 temperament(F=2.825, p=.045) of MBTI. Further study is necessary to provide the basic data to construct the curriculum of nursing health education which is suitable for the characteristic of MBTI personality type.

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

In this paper, we describe a permutation procedure in which we compute a resampling probability value and empirical quantile limits for Split-Half measure of internal reliability. We use the Split-Half reliability coefficient given by two simple methods, the Spearman-Brown formula and the two-part coefficient alpha. The use of a permutation test for Split-Half reliability coefficient is highlighted as a valuable tool when the sample sizes are small and necessary assumptions cannot be met. The permutation tests for Split-Half reliability coefficient are illustrated with an example analysis of two survey data with a sample size of 15 and 35, respectively, and a hypothetical data with a sample size of 5.

As the importance of technology increases, so too does its use in various areas. In education, technology is often used. However, due to teachers' lack of knowledge about technology, they often remain at the level of simple utilization, without applying it to learning. Thus, there is a growing need for Technological Pedagogical Content Knowledge (TPACK), which enables teachers to have knowledge about technology and use it appropriately given the content. Although TPACK studies are underway in many subjects, they suffer from the limited functionality of the included technology. To solve this problem, in this study, the range of technology in TPACK was extended to programming, and a TPACK-P model was developed to teach this expanded TPACK to pre-service teachers. To verify the effectiveness of this model, the TPACK-P training program developed during the 15th classes was applied to 19 pre-service teachers. We used Park and Kang (2014) as a tool to measure these teachers' TPACK before and after treatment to observe any changes. The results showed that the TPACK-P education program showed statistically significant improvement in all areas except Pedagogical Content Knowledge(PCK). Compared with the ICT-based TPACK training program, which was administered to a control group, the TPACK-P training program proved to be more effective in the development of Technological Pedagogical Knowledge(TPK) and TPACK among pre-service teachers.