• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.4
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• pp.27-32
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• 2007

Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification, we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes, we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied. The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.

• International Journal of Costume and Fashion
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• v.13 no.2
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• pp.65-77
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• 2013

The purposes of this study were to document the fashions adopted by young women in the United States in the 1980s and to explore if and how the dynamic shifts toward postmodernist values influenced those fashion trends. Fifteen U.S. women who were college students in the 1980s were interviewed for the study. In analysis of the data, we focused on social changes during the 1980s and the cultural impact of postmodernism vs. modernism as influential factors. Both postmodern and feminist ideas challenged the mainstream cultural framework of capitalism. U.S. women's styles and behaviors concerning dress reflected characteristics of postmodern consumption patterns, which include nostalgia, ethnic dress, androgyny, eclectic and novel clothing combinations, surprising or humorous appearance, and nonconformity. Despite the critique of conformity and conservatism in dress that had emerged in the 1960s and remained in at least minority or subversive trends, the importance of brand names and designer labels increased in mainstream fashion. This study helps us better understand the dynamics of fashion as it reflects societal and value changes in a transitional time in history.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.835-854
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• 2012

In this paper, we propose a novel positioning system for indoor navigation which helps a user navigate easily to desired destinations in an unfamiliar indoor environment using his mobile phone. The system requires only the user's mobile phone with its basic equipped sensors such as a camera and a compass. The system tracks user's positions and orientations using a vision-based approach that utilizes $360^{\circ}$ panoramic images captured in the environment. To improve the robustness of the vision-based method, we exploit a digital compass that is widely installed on modern mobile phones. This hybrid solution outperforms existing mobile phone positioning methods by reducing the error of position estimation to around 0.7 meters. In addition, to enable the proposed system working independently on mobile phone without the requirement of additional hardware or external infrastructure, we employ a modified version of a fast and robust feature matching scheme using Histogrammed Intensity Patch. The experiments show that the proposed positioning system achieves good performance while running on a mobile phone with a responding time of around 1 second.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.567-582
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• 2011

The growing popularity of the Internet has caused the size and complexity of communications and computing systems to greatly increase in recent years. To alleviate this increased management complexity, novel autonomic management architectures have emerged, in which many automated components manage the network's resources in a distributed fashion. However, in order to achieve effective collaboration between these management components, they need to be able to efficiently exchange information in a timely fashion. In this article, we propose a context dissemination framework that addresses this problem. To achieve scalability, the management components are structured in a hierarchy. The framework facilitates the aggregation and translation of information as it is propagated through the hierarchy. Additionally, by way of semantics, context is filtered based on meaning and is disseminated intelligently according to dynamically changing context requirements. This significantly reduces the exchange of superfluous context and thus further increases scalability. The large size of modern federated cloud computing infrastructures, makes the presented context dissemination framework ideally suited to improve their management efficiency and scalability. The specific context requirements for the management of a cloud data center are identified, and our context dissemination approach is applied to it. Additionally, an extensive evaluation of the framework in a large-scale cloud data center scenario was performed in order to characterize the benefits of our approach, in terms of scalability and reasoning time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1922-1940
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• 2019

IP address lookup is a function to determine nexthop for a given destination IP address. It takes an important role in modern routers because of its computation time and increasing Internet traffic. TCAM-based IP lookup approaches can exploit the capability of parallel searching but have a limitation of its size due to latency, power consumption, updatability, and cost. On the other hand, multibit trie-based approaches use SRAM which has relatively low power consumption and cost. They reduce the number of memory accesses required for each lookup, but it still needs several accesses. Moreover, the memory efficiency and updatability are proportional to the number of memory accesses. In this paper, we propose a novel architecture using an Indexed Multibit Trie (IMT) which is based on combined TCAM and SRAM. In the proposed architecture, each lookup takes at most two memory accesses. We present how the IMT is constructed so as to be memory-efficient and fast updatable. Experiment results with real-world forwarding tables show that our scheme achieves good memory efficiency as well as fast updatability.

• Journal of Power Electronics
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• v.19 no.4
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• pp.858-868
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• 2019

With the widespread use of modern clean energy, lithium-ion batteries have become essential as a more reliable energy storage component in the energy Internet. However, due to the difference in monomers, some of the battery over-charge or over-discharge in battery packs restrict their use. Therefore, a novel multiphase interleaved converter for reducing the inconsistencies of the individual cells in a battery pack is proposed in this paper. Based on the multiphase converter branches connected to each lithium battery, this circuit realizes energy transferred from any cell(s) to any other cell(s) complementarily. This flexible interlaced converter is composed of an improved bi-directional Buck-Boost circuit that is presented with its own available control method. A simulation model based on the PNGV model of fundamental equalization is built with four cells in PSIM. Simulation and experimental results demonstrate that converter and its control achieve simple and fast equalization. Furthermore, a comparison of traditional methods and the HNFABC equalization is provided to show the performance of the converter and the control of lithium-based battery stacks.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.75-86
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• 2021

As artificial intelligence is actively studied, chatbot systems are being applied to various fields. In particular, many chatbot systems for psychological counseling have been studied that can comfort modern people. However, while most psychological counseling chatbots are studied as rule-base and deep learning-based chatbots, there are large limitations for each chatbot. To overcome the limitations of psychological counseling using such chatbots, we proposes a novel psychological counseling AI chatbot system. The proposed system consists of a GPT-2 model that generates output sentence for Korean input sentences and an Electra model that serves as sentiment analysis and anxiety cause classification, which can be provided with psychological tests and collective intelligence functions. At the same time as deep learning-based chatbots and conversations take place, sentiment analysis of input sentences simultaneously recognizes user's emotions and presents psychological tests and collective intelligence solutions to solve the limitations of psychological counseling that can only be done with chatbots. Since the role of sentiment analysis and anxiety cause classification, which are the links of each function, is important for the progression of the proposed system, we experiment the performance of those parts. We verify the novelty and accuracy of the proposed system. It also shows that the AI chatbot system can perform counseling excellently.

• Journal of Pharmacopuncture
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• v.24 no.4
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• pp.165-172
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• 2021

The COVID-19, the most infectious pandemic disease arising due to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused huge issues globally. In this review, we discuss the impact of COVID-19 on the immune system of the human body and the protective mechanisms of the host immune system opposing viral infections. Here, we summarize the effect of the pandemic of the novel coronavirus disease on the immune system such as sleep and Behavioral Immune System (BIS) together with consideration of researcher's observation points of view. We draw particular attention to recent up-to-date reports concerning COVID-19 drugs as well as information about the landscape document for COVID-19 vaccines released by WHO (World Health Organization), and some adverse events of COVID-19 vaccination. Additionally, can take part in the preventive appraise in opposition within this pandemic severe COVID-19 infections disease may affect some outcome in physical exercise, physical movement, healthy diets, and good nutrition are significant for supporting the immune systems and summarize AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy) Indian medicinal systems guidelines for immunity boosting procedures during COVID-19 pandemic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4122-4144
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• 2021

In this fast-paced technological world, the Internet of Things is a ground breaking technology which finds an immense role in the present electronic world which includes different embedded sensors, devices and most other things which are connected to the Internet. The IoT devices are designed in a way that it helps to collect various forms of data from varied sources and transmit them in digitalized form. In modern era of IoT technology data security is a trending issue which greatly affects the confidentiality of important information. Keeping the issue in mind a novel light encryption strategy known as LCB is designed for IoT devices for optimal security. LCB exploits the benefits of Feistel structure and the architectural benefits of substitution permutation network both to give more security. Moreover, this newly designed technique is tested on (Virtex-7) XC7VX330T FPGA board and it takes much little area of 224 GE (Gate Equivalent) and is extremely fast with very less combinational path delay of 0.877 ns. An in-depth screening confirms the proposed work to promise more security to counter cryptographic attacks. Lastly the Avalanche Effect (AE) of LCB showed as 63.125% and 63.875% when key and plaintext (PT) are taken into consideration respectively.