• Journal of Information Science Theory and Practice
• /
• v.8 no.4
• /
• pp.6-19
• /
• 2020

The objective of this research is to analyze which functions make up web-based as well as mobile social media services and messengers. Services are clustered by their functionality. A total of 640 individual functions were identified, while investigating altogether 44 selected services in their web and mobile versions. Applying content analysis, functions were assigned to the services. The services were ranked by the number of implemented functions, and the functions were ranked by their occurrence in the services. Cluster analysis was applied to classify the services according to their functionality. Facebook and VKontakte were found to be the ones with the most functions; the most frequently implemented functions are support, profile, and account-related. Cluster analysis revealed six classes for mobile and seven classes for web applications. There is a noteworthy difference regarding the functionality scope between web and mobile applications of the same services. An example for this is Mendeley with 38 functions in the mobile and 91 functions in the web version. This is the first empirical attempt at clustering social media services based on their functionality.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1276-1295
• /
• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.2
• /
• pp.312-320
• /
• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.3
• /
• pp.90-99
• /
• 2016

Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

• Journal of the Korea Safety Management & Science
• /
• v.16 no.1
• /
• pp.119-127
• /
• 2014

This study tried to develop a basis for quantitative index of working postures associated with WMSDs (Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data(for a total of 603 jbs) for this study was obtained from automobile manufacturing company. Specifically, data for back posture was analyzed in this study to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were clustering, logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationship between working posture and WMSDs symptom at back was statistically significant based on the results from logistic regression, 2) Based on clustering analysis, three levels for WMSDs risk at back were produced for flexion as follows: low risk(< $18.5^{\circ}$), medium risk($18.5^{\circ}{\sim}36.0^{\circ}$), high risk(> $36.0^{\circ}$), 3) The sensitivities on risk levels of back flexion was 93.8% while the specificities on risk levels of back flexion was 99.1%. The results showed that the data associated with back postures in this study could provide a good basis for job evaluation of WMSDs at back. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as back would provide more stability and reliability in WMSDs evaluation study.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.2
• /
• pp.436-444
• /
• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Journal of the Korean Society of Safety
• /
• v.28 no.1
• /
• pp.95-101
• /
• 2013

This study tried to develop a basis for quantitative index of working postures associated with WMSDs(Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data for this study was obtained from automobile manufacturing company(A total of 603 jobs were observed). Specifically, data for shoulder postures was analyzed to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were Clustering, Logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationships between working postures and WMSDs symptoms at shoulder were statistically significant based on the results from logistic regression. 2) Based on clustering analysis, three levels for WMSDs risk at shoulder were produced for both flexion and abduction were statistically significant. Specific results were as follows; Shoulder flexion: low risk(< $37.7^{\circ}$), medium risk($37.7^{\circ}{\sim}70.0^{\circ}$), high risk(> $70.0^{\circ}$) Shoulder abduction: low risk(< $26.5^{\circ}$), medium risk($26.5^{\circ}{\sim}56.8^{\circ}$), high risk(> $56.8^{\circ}$). 3) The sensitivities on risk levels of shoulder flexion and abduction were 64.0% and 20.6% respectively while the specificities on risk levels of shoulder flexion and abduction were 99.1% and 99.3% respectively. The results showed that the data associated with shoulder postures in this study could provide a good basis for job evaluation of WMSDs at shoulder. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as shoulder would provide more stability and reliability in WMSDs evaluation study.

• The KIPS Transactions:PartB
• /
• v.12B no.7 s.103
• /
• pp.755-760
• /
• 2005

To transmit a video bit stream over low bandwith, such as mobile, channels, encoding algorithms for high bit rate like H.263+ are used. In transmitting video bit-streams, packet losses cause severe degradation in image quality. This paper proposes a new algorithm for the recovery of missing or erroneous motion vectors when H.263+ bit-stream is transmitted. Considering that the missing or erroneous motion vectors are closely related with those of neighboring blocks, this paper proposes a temporal-spatial error concealment algorithm. The proposed approach is that missing or erroneous Motion Vectors(MVs) are recovered by clustering the movements of neighboring blocks by their homogeneity. MVs of neighboring blocks we clustered according to ALA(Average Linkage Algorithm) clustering and a representative value for each cluster is determined to obtain the candidate MV set. By computing the distortion of the candidates, a MV with the minimum distortion is selected. Experimental results show that the proposed algorithm exhibits better performance in subjective and objective evaluation than existing methods.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.630-637
• /
• 2018

Harmony search (HS) is a recently developed metaheuristic optimization algorithm. HS is inspired by the process of musical improvisation and repeatedly searches for the optimal solution using three operations: random selection, memory recall (or harmony memory consideration), and pitch adjustment. HS has been applied by many researchers in various fields. The increasing complexity of real-world optimization problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms and HS are required. We propose an improved clustering harmony search (ICHS) that uses a clustering technique to group solutions in harmony memory based on their objective function values. The proposed ICHS performs modified harmony memory consideration in which decision variables of solutions in a high-ranked cluster have higher probability of being selected than those in a low-ranked cluster. The ICHS is demonstrated in various optimization problems, including mathematical benchmark functions and water distribution system pipe design problems. The results show that the proposed ICHS outperforms other improved versions of HS.

• Clinical and Experimental Reproductive Medicine
• /
• v.33 no.4
• /
• pp.253-263
• /
• 2006

Objective: Identification of the regulatory mechanism for arrest and initiation of primordial follicular growth is crucial for female fertility. Previously, we found 15 expressed sequence tags (ESTs) that were specifically abundant in the day-S-subtracted cDNA library and that the B357 clone was novel. The present study was conducted to obtain the whole sequence of the novel gene including B357 and to characterize its mRNA and protein expression in mouse ovary and testis. Methods: The extended sequence of the 2,965-bp cDNA fragment for the clone B357 was named ${\underline{5}}-{\underline{d}}ay-{\underline{o}}vary-{\underline{s}}pecific\;gene-{\underline{1}}$ (5DOS1) and submitted to GenBank (accession number ${\underline{AY751521}}$). Expression of 5DOS1 was characterized in both female and male gonads at various developmental stages by Northern blotting, real-time RT-PCR, in situ hybridization, Western blotting, and immunohistochemistry. Results: The 5DOS1 transcript was highly expressed in the adult testis, brain, and muscle as compared to the other tissues. In the ovary, the 5DOS1 transcript was detected in all oocytes from primordial to antral follicles, and highly expressed at day 5 after birth and decreased thereafter. In contrast, expression of 5DOS1 showed a gradual increase during testicular development and its expression was limited to various stages of male germ cells except spermatogonia. Conclusions: This is the first report on the expression and characterization of the 5DOS1 gene in the mouse gonads. Further functional analysis of the 5DOS1 protein will be required to predict its role in gametogenesis.