• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.318-330
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• 2015

In wearable sensor systems (WSSs), sensor nodes are deployed around human body parts such as the arms, the legs, the stomach, and the back. These sensors have limited lifetimes because they are battery-operated. Thus, transmission power control (TPC) is needed to save the energy of sensor nodes. The TPC should control the transmission power level (TPL) of sensor nodes based on current channel conditions. However, previous TPC algorithms did not precisely estimate the channel conditions. Therefore, we propose a new TPC algorithm that uses an accelerometer to directly measure the current channel condition. Based on the directly measured channel condition, the proposed algorithm adaptively adjusts the transmission interval of control packets for updating TPL. The proposed algorithm is efficient because the power consumption of the accelerometer is much lower than that of control packet transmissions. To evaluate the effectiveness of our approach, we implemented the proposed algorithm in real sensor devices and compared its performance against diverse TPC algorithms. Through the experimental results, we proved that the proposed TPC algorithm outperformed other TPC algorithms in all channel environments.

• Journal of Power Electronics
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• v.14 no.4
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• pp.671-677
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• 2014

In this paper, a new basic unit is proposed. Then, a cascaded multilevel inverter basded on the series connection of n number of these new basic units is proposed. In order to generate all of the voltage levels (even and odd) at the output, three different algorithms to determine the magnitude of the dc voltage source are proposed. Reductions in the number of power switches, driver circuits and dc voltage sources in addition to increases in the numbr of output voltage levels are some of the advantages of the proposed cascaded multilevel inverter. These results are obtained through a comparison of the proposed inverter and its algorithms with an H-bridge cascaded multilevel inverter from the point of view of the number of power electronic devices. Finally, the capability of the proposed topology with its proposed algorithms in generating all of the voltage levels is verified through experimental results on a laboratorary prototype of a 49-level inverter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.364-370
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• 2014

The management and analysis of spatio-temporal and multimedia data is a hot issue in database research because such data types are handled in manny applications. Querying databases of such a content is very important for these applications. This paper addresses algorithms that make index structure by using Douglas-Peucker Algorithm and process range query efficiently on moving objects trajectories. We compare and analyze our algorithms and MBR by experiments. Our algorithms make smaller size of index structure and process more efficiently.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.1118-1134
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• 2023

Previous research has shown that employees tend to react more positively to corrective feedback from supervisors to the extent they perceive that they were treated with empathy, respect, and concern towards fair interpersonal treatment in receiving the feedback information. Then, to facilitate effective supervisory feedback and coaching, it would be useful for organizations to monitor the contents of feedback exchanges between supervisors and employees to make sure that supervisors are providing performance feedback using languages that are more likely to be perceived as interpersonally fair. Computer-aided text analysis holds potential as a useful tool that organizations can use to efficiently monitor the quality of the feedback messages that supervisors provide to their employees. In the current study, we applied computer-aided text analysis (using closed-vocabulary text analysis) and machine learning to examine the validity of language-based algorithms trained on supervisor language in performance feedback situations for predicting human ratings of feedback interpersonal fairness. Results showed that language-based algorithms predicted feedback interpersonal fairness with reasonable level of accuracy. Our findings provide supportive evidence for the promise of using employee language data for managing (and improving) performance management in organizations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.537-546
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• 2001

Griffth's and LCCMA which are Single-user detection adaptive algorithm are proposed for mitigate MAI(multiple access interference) and the near-far problem in direct-sequence spread-spectrum CDMA system and MOE Algorithm is proposed for MMSE(Minimum Mean-Square Error). This paper pertains to three types of Blind adaptive algorithms which can upgrade system functionality without the requirements from training sequence. It goes further to compare and analyze the functionalities of the algorithms as per number of interfering users or data update rate of the users. The simulation results was that LCCMA algorithm was superior to other algorithms in both areas. Blind application enabled a more flexible network design by eliminating the necessity of training sequence.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.18-23
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• 2003

This paper presents new algorithms of path planning and obstacle avoidance for an autonomous mobile robot to navigate under unknown environments in the real time. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. It is shown by computer simulation that these algorithms are working well. Furthermore, an autonomous mobile robot was constructed to implement and test these algorithms in the real field. The experimental results are also satisfactory just like those of computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.