Search

Quantum modular multiplication circuit is one of the basic quantum computation circuits which are basic functions in quantum algorithms. However, since quantum-quantum modular multipliers require a high cost reversible modular inversion routine for modular multiplication, researchers have been unable to propose a feasible quantum-quantum modular multiplier. In this paper, we proposed efficient quantum-classical modular multipliers and the first quantum-quantum modular multipliers that do not require a reduction stage by transforming the partial product used in multiplication utilizing bit-shift operation. Then, we calculated quantum resource complexity and analyzed it compared to other quantum modular multipliers and utilized ETRI (Electronics and Telecommunications Research Institute) Qcrypton to analyze quantum resource complexity in the practical quantum computing situation. The proposed quantum modular multipliers show an improvement of 50% in terms of gates and circuit depth compared to the most recently proposed high-performance quantum modular multipliers.

This paper presents an investigation of the electromagnetic signature and the coupling mechanism of quadcopter drones with incident electromagnetic (EM) wave and radar cross section (RCS) analysis. Coupling analysis is performed based on the dominant coupling path: when an incident EM wave with a magnitude of 50 kV/m contacts a commercial quadcopter drone, its motor power wires are identified as the dominant coupling path. Higher coupling voltages are obtained for frequencies that have large impedance values at both ends of the load on the motor power wire. This induced voltage can affect the integrated circuit chip on a printed circuit board, as well as parallel plate resonances. Furthermore, the RCS of a quadcopter drone is measured in the frequency range of 0.5-3 GHz. The internal-component vulnerability characteristics of quadcopters can spike at specific frequencies with high RCS values and can be analyzed with or without motor power wires. We verified these hypotheses via 2D inverse synthetic aperture radar images, and we analyzed the results by comparing the empirical and full-wave simulation values.

Today’s export-based manufacturers face the need to implement green supply chains more efficiently in order to overcome environmental barriers in increasingly competitive export markets and to improve export performance. This study examines the structural relationship between environmental collaboration, green innovation capacity, and performance based on the findings of previous studies in order to identify the factors affecting the green supply chain management (GSCM) performance of Korean export-based manufacturers. The study finds that environmental collaboration in the green supply chain environment is an important driver of green innovation capacity for export-based Korean manufacturers. It also finds that green innovation capacity has a positive effect on export performance through environmental performance. This study establishes a theoretical basis for the systematic study of the structural mechanisms of green supply chains and suggests strategic directions for export-based manufacturing firms’ successful GSCM implementation.

This study aims to analyze the characteristics of technology innovation for domestic small and medium size enterprises by measuring a technology innovation effect level and analyzing technology innovation effect characteristics. The characteristics of study in this respect are to analyze both technology innovation effect based on three factors, product, process, and organization, by using Korea Innovation Survey Data, and characteristics of causal among innovation effects. In addition, the study has suggested a technology innovative diffusion pathway by analyzing innovation dynamics characteristics which has not been researched from pre-existed study.

The induction heating power supplies for forgings applications generally adopt a current source inverter driven parallel resonant circuit system due to fault tolerant characteristic and easily obtainable large current for rapid heating. Many converter topologies to get the DC current source of current source inverter has been studied for high power factor and low THD characteristics. This paper proposes a new high-power factor induction heating power supply using three-phase three-switch PWM current source rectifier for forging applications. It also presents the design guidelines of a current source rectifier/inverter-fed parallel resonant circuit. The proposed induction heating power supply is expected to be widely used for forging applications.

This paper presents an image compression method using deep learning based super-resolution. Different from traditional image compression methods, we propose to select the super resolution network to enhance the perceptual quality. In the proposed method, the network selection map is generated by utilizing the semantic segmentation, image classification, and encoding distortion. Compared with traditional scalable image coding methods, more texture area are visually pleased even in the severe low bitrate encoding. For this reason, our method has the potential to result better compression ratio in terms of subjective image quality. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding (HEVC), our method achieve the average of 43.42% bitrate saving at the same perceptual image quality.

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

In this paper, a low bit-rate compressed image quality enhancement framework is presented. A recent image/video coding method and a deep learning based quality enhancement method are integrated to improve the perceptual quality of compressed images. The proposed architecture is designed to reduce the coding artifact and restore the blurred texture details. The experimental results presents that the proposed framework yields a 33% improvement in the Perceptual Index score which is consistent with visual evaluation on a sample of results.

Welding is one of the basic tasks required in various industries as well as manufacturing. The purpose of this study is to develop a haptic torch to create an environment similar to actual welding in order to use a welding simulator as a training tool. In order to develop a torch with a haptic technology, we created a torch using a 3D printer and reduced the error by increasing the position accuracy using the Polhemus simulation tool. Using a welding simulator with a haptic torch capable of controlling the intensity of a vibration motor, we found that it was very similar to actual welding.

Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in high-dimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks - with biological and chemical structure data. Our proposed model outperforms previous learning-based methods in terms of prediction accuracy.

A flexible vibrotactile actuator based on a wavy shaped silicone rubber has been proposed. The proposed actuator consists of the wavy silicone rubber sandwiched between upper and lower thin electrode layers. When the AC signal is applied to the proposed actuator, the actuator generates vibration by the attractive electrostatic force and the elastic restoring force. In this work, we evaluate the frequency response of the proposed vibrotactile actuator through a quantitative experiment. The maximum acceleration is observed as 0.728g at a frequency of 128 Hz and at an amplitude of 2.5 kV/ mm. Furthermore, the accelerations by varying the applied electric field are measured at the resonance frequency. The experimental results show that the proposed actuator can be used in next-generation wearable devices.

Accurate identification of sentinel lymph nodes (SNL) and complete tumor removal in cancer patients is of critical importance and leads to improved patient recovery and survival. Gamma probes along with near infrared (NIR) probes are the gold standard in SNL identification but have failed to reduce false negative rates (FNR) to the desirable 5% value. By developing a high-resolution Positron Emission Tomography (PET) detector atop the patient in coincidence with a partially populated detector ring placed underneath the patient bed with

In this paper, we propose a transparent and soft vibrotactile actuator based on silicone rubber. Silicone rubber, which is the nonionic dielectric elastomer, was sandwiched between ionic polyacrylamide hydrogels. Silicone rubber is repetitively compressed and expanded by the electric field generated by lithiated hydrogels, which generates the vibration. We systematically explore the vibration acceleration of silicone interface between ionic hydrogels based on the frequency and voltage of input signal. This proposed vibrotactile actuator significantly generates the vibration in the frequency range 1 Hz to 500 Hz. Furthermore, the peak resonant (1.016 g) frequency is observed at 290 Hz. The detailed investigation suggests that the developed actuator can be controlled by the input voltage in the range of 0.1 kV to 2.5 kV. The proposed vibrotactile actuator can satisfactorily be utilized in deformable or transparent devices such as smart windows, wearable devices, and transparent display equipment.

Intrusion detection system(IDS) is one of the widely adapted security systems in the world. IDS can detect malicious activities on networks or hosts and raises alerts which should be analysed by security operators. But alerts generated from IDS are too huge to analyse all of them and most of the alerts are false positive. To mitigate this problem, many approaches have been carried out using machine learning. Machine learning(ML) is a promising technique because of its outstanding performance. But sometimes it is time and effort consuming work to make a good machine learning model. Because one should consider many options on the machine learning making process including feature, normalization and model. And there should be many repetitive experiments to find proper parameters of model. In this paper, we propose a machine learning platform for classification of IDS alerts as a way of solution about the time and effort consuming works. On the platform, researcher and engineers don't need to care about implementation and can focus only on better configurations for classification of IDS alerts. Repetitive works of data processing and building models will be carried out in an automated manner on the platform. As well, we expect that many security experts unfamiliar with machine learning technique can attempt to make machine learning models easily through the platform. With the participation of many security experts, various experiments could be carried out. And it could promote the transformation of their expertise into ideas for models through their feedback. This paper presents how we designed our platform and implemented the system until these days.

Induction heating can convert electrical energy to thermal energy with high conversion efficiency and quick heating. Currently, a current source rectifier/inverter-fed parallel resonant circuit is widely used as an induction heating power supply for forging applications. However, the conventional induction heating power supplies composed of phase-controlled rectifier and SCR inverter have low efficiency and low power factor at input side, and require additional starting circuitry. Thus, many topologies have been studied for high power factor and low current THD characteristics. This paper proposes the new high power factor induction heating power supply using 3-phase, 3-switch PWM rectifier with power factor control and low cost and simple control. Also, this paper proposes a design method for the proposed power supply and verifies the validity of the proposed power supply through simulation.

In this paper, we propose the novel methodology to improve the rate-distortion characteristics by the difference of the quantized DCT coefficients using two different quantized parameters in HEVC. Under the special condition of the quantization property in HEVC, we evaluate the binary difference of the quantized DCT coefficients derived from two different quantized parameters and compressed it with simple CABAC algorithm based on the characteristics of the binary difference.The experimental result shows that the proposed algorithm improves the compression performance of the rate-distortion property in comparison to the conventional HEVC compression method.

This paper presents initial design and demonstration for an interactive drilling simulation. We have developed a new haptic feedback drill interface whose outer figure is identical to a conventional electric drill. The proposed system is designed for providing haptic responses to users such as contact response, thrust force, machinery vibration, torsional forces and the edge penetration force occurred during drilling task. In addition, interactive auditory and visual feedback are also provided together for more realistic multimodal interaction.

Detecting major system anomalies with observed multivariate time series requires not only the characteristics of each time series but also the status of the entire time series dynamics. Therefore, we propose a method that can detect substantial anomalies by generating a transfer network and an influence network from a multivariate time series. To form a transfer network, each vertex represents a single time series. Each edge indicates the strength of the information flow between each pair of time series using transfer entropy. With the transfer network, we exploit the random walk approach to calculate the affinity score between two vertices and create an influence network that reflects both the direct and indirect influences. In our experiment, we show the efficacy of the proposed method using simple synthetic time series networks and the real data set such as world stock indices and key performance indicators of the SAP HANA in-memory database system.

A multi-level thresholding method for image segmentation is presented. The image is divided into multiple regions based on the grayscale level. The procedure for finding best thresholds is implemented by a genetic algorithm with appropriate coding method. The experiment results show that the proposed method gives a good performance.

Induction heating can convert electrical energy to thermal energy with high conversion efficiency and quick heating. Currently, a current source rectifier/inverter-fed parallel resonant circuit is widely used as an induction heating power supply for forging applications. However, the conventional induction heating power supplies composed of phase-controlled rectifier and SCR inverter have low efficiency and low power factor at input side, and require additional starting circuitry. So this paper proposes new induction heating power supply topologies for forging applications which have high power factor, high efficiency, and large output power, and compares their characteristics and performances.

In this paper, extraction of disparity information using pixels with integrated apertures is presented. In general, the aperture of a camera is located over the camera lens. However, we propose the integration of apertures on the pixels of a CMOS image sensor. The integrated apertures are designed and fabricated using a metal layer of the CMOS image sensor process and the parameters including width and offset of the integrated apertures are optimized through optical simulation. The offset of the integrated aperture is defined as the distance between the center of the pixel and the center of the integrated aperture in a unit pixel. The optical simulation is based on a finite-difference time-domain method. Based on the optical simulation, the proposed image sensor with integrated apertures was fabricated using the CMOS image sensor process. The disparity information of the images is extracted using the fabricated image sensor and the performance of this sensor is evaluated through experiments.

The purpose of this paper is to solve the social pathological problems through the application of scientific and systematic training methods in response to the demand for improvement of muscular strength of elderly people. It is expected that it will be used as the evaluation, strengthening of muscular function, and rehabilitation exercise program through the provision of exercise information and training program. This paper introduces exercise equipment for the muscular strength of elderly, which provides measurement data and training programs. Though control module and training information module, training load is controlled for safety and effective exercise.

In this paper, complementary metal oxide semiconductor (CMOS) image sensors that can extract depth information using the pixel aperture technique is presented. The pixel array of the proposed image sensor is composed of blue, red, and white pixels, as well as pixel apertures. The apertures are formed by metal pattern in the white pixels. The focused and defocused images are obtained, simultaneously, using the proposed image sensor, and are used for calculating the depth information. The proposed image sensor was designed and fabricated using the 0.11-μm CMOS image sensor process, and its performance was evaluated.

Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.

In this paper, we propose a pixel current calibration technique for reducing the fixed pattern noise due to the deviations in the bolometer resistance. Usually, it is difficult to calibrate the fixed pattern noise of a bolometer array. The principle of the pixel current calibration technique is that the dark current of a reference pixel is subtracted from the dark current of each active pixel. To reduce the difference in current due to the pixel resistance variation, a suitable calibration current is required. Through this pixel current calibration technique, the dark current of the active pixel resistor, and subsequently, the fixed pattern noise, can be reduced significantly. A readout circuit using the proposed pixel current calibration technique is simulated and fabricated using the 0.35 μm standard CMOS process.

The spiral termination helical antenna is proposed in this paper. To reduce axial length, the number of helical turns is selected to be two turns and the pitch angle is selected to be 2.5°. The axial length of the proposed antenna is 0.15 λ 0 (46 mm). It is reduced to 80% of conventional low-profile helical antenna. The gain of proposed antenna is 9.6 dB and 3 dB axial ratio bandwidth is 10%.

This paper presents a CMOS image sensor that uses the pixel aperture technique for extracting depth information while producing conventional 2D images. In conventional image sensors, the aperture is located at the camera lens. However, in the proposed image sensor, the aperture is integrated on the CMOS image sensor chip and is formed at a metal layer of the CMOS process. The pixel array of the proposed image sensor, used for extracting color and depth information, is composed of four different types of pixels — white pixels with aperture, blue pixels, green pixels, and white pixels without aperture. The size of the pixel, which is based on a four-transistor active pixel sensor with pinned photodiode, is 2.8 μm × 2.8 μm. A prototype of the proposed image sensor was fabricated using the 0.11 μm CMOS image sensor process, and the feasibility of the pixel aperture technique was verified.

With the development of cyber threats on the Internet, the number of malware, especially unknown malware, is also dramatically increasing. Since all of malware cannot be analyzed by analysts, it is very important to find out new malware that should be analyzed by them. In order to cope with this issue, the existing approaches focused on malware classification using static or dynamic analysis results of malware. However, the static and the dynamic analyses themselves are also too costly and not easy to build the isolated, secure and Internet-like analysis environments such as sandbox. In this paper, we propose a lightweight malware classification method based on detection results of anti-virus software. Since the proposed method can reduce the volume of malware that should be analyzed by analysts, it can be used as a preprocess for in-depth analysis of malware. The experimental showed that the proposed method succeeded in classification of 1,000 malware samples into 187 unique groups. This means that 81% of the original malware samples do not need to analyze by analysts.

Induction heating can convert electrical energy to thermal energy with high conversion efficiency and quick heating. Currently, a current source rectifier/inverter-fed parallel resonant circuit is widely used as an induction heating power supply for forging applications. However, the conventional induction heating power supplies composed of phase-controlled rectifier and SCR inverter have low efficiency and low power factor at input side, and require additional starting circuitry. So this paper proposes new induction heating power supply topologies for forging applications which have high power factor, high efficiency, and large output power, and compares their characteristics and performances.

In this paper, an active phased-array transmitter that can be used to generate 1-kW peak pulsed output is presented. It consists of 192-channel transmit modules and each transmit module with greater than 6 W of CW output power is connected to rigid tapered slot antenna in the frequency range 6–18 GHz. In transmit module, integrated multi-function corechip is designed in order to individually control both amplitude and phase of array antennas. Far-field radiation pattern of active phased-array transmitter was obtained using field-transformation method after carrying out near-field measurement in anechoic chamber. Electronic beam steering capability of active phase-array transmitter was demonstrated with a scan angle of ±20° in both azimuth and elevation angle. The overall system size is 840×480×630 mm3.

With the development of software and devices for next generation sequencing, a vast amount of bioinformatics data has been generated recently. Also, bioinformatics data based big-data technology is rising rapidly as a core technology by the bio-informatician, biologist and big-data scientist. Especially, most big data analyses are based on network analyses because it can discover characteristics and patterns between data in a network. However, a biological network analysis requires a lot of time and effort because biological networks are high volume and very diverse. In this paper, we proposed a network crawling, clustering and visualization system that crawls literatures and papers from user interest a web site, constructs a biological network based on a hierarchy structure of biological entities and relations extracted from sentences in the literatures and papers and visualizes relations and interactions of the network by clustering and selecting core nodes from the network. Finally, we construct a Alzheimer's disease network collected from PubMed and show the results on clustering and selecting core nodes from the network.

In cognitive radio technology, the overall capacity of communications systems can be improved without allocating additional bands by allowing secondary system to utilize the licensed band when a primary system who has right to use the band does not use it. In this paper, we consider how to set up a common channel for data communication in an ad-hoc networks consisting of a cluster head and member nodes using a cognitive radio technology. We propose a new initial rendezvous protocol that uses the system activation signal which has angle difference patterns with multicarrier operations.

Lately, research on bolometer type uncooled infrared image sensor has been increasing for industrial applications. But it is hard to remove a Fixed Pattern Noise (FPN) of bolometer sensor. In this paper, a novel average-current calibration algorithm is presented for reducing bolometer resistance offset. A resistor which is produced standard CMOS process, on the average, has a deviation, respectively. We compensate for deviation of each active resistor using average-current calibration algorithm. The algorithm consists of as follows: bolometer pixel array, average current generator, current-to-voltage converters (IVCs), digital-to-analog converter (DAC), and analog-to-digital converters (ADCs). These bolometer-resistor array and readout circuit were designed and manufactured by 0.35μm standard CMOS process.

Recently there have been widespread used distributed generators and sensitive loads. It enables solid-state circuit breaker (SSCB) an imperative device to get acceptable power quality of AC power grid systems. The existing AC SSCB composed of SCRs requires some auxiliary mechanical devices to achieve the reclosing operation before fault recovery. But the new AC SSCB can achieve the quick breaking operation and then be reclosed with no auxiliary mechanical devices even under sustained short-circuit fault. Furthermore, the proposed AC SSCB can be applied regardless of neutral point grounded method of 3-phase power source, and also all the commutation capacitors are charged naturally without any complex control of main thyristors and auxiliary ones. The performance features of the proposed AC SSCB are verified through simulations of the short-circuit faults. The proposed AC SSCB is expected to be widely used to design and realize a lot of AC grid systems.

Magnetic nanoparticles (NPs) generate growing interest of scientists not only from physics and chemistry areas, but also from biomedicine including cancer research. By means of self-organization, NPs can form agglomerates, superlattices or ordered arrays consisting of finite number of nanoparticles. The understanding of magnetic behavior of coupled NPs in chains will open up new routes of their practical usage.

The continuous optimization of manufacturing processes is important to achieve a success on the market. Once analyzed, the data, collected from various manufacturing resources, can reveal the useful knowledge. We can use this knowledge for the optimization of manufacturing process. However, due to the massive amount of data, discovering the knowledge hidden in the data can be a challenging task. In this paper, we propose an integrated manufacturing data visualization method using the calendar view tool. The main idea of the proposed method is to visualize and identify the issues and outliers that may occur during the manufacturing process on a daily basis.

Parametric array transducers are used for highly directional speaker in an air environments. Piezoelectric micromachined ultrasonic transducers for parametric array transducers need DC-biased voltage driving signals in order to get high-directional quality-sound features. The existing power amplifier such as class A amplifiers has low efficiency and require large volume heatsinks. To overcome the above-mentioned disadvantages of the conventional amplifier, this paper proposes a new power amplifier system. The proposed power amplifier system ensures high linearity of output characteristic by utilizing the push-pull class B type amplifier. Furthermore, the proposed power amplifier system gets high efficiency because it contains the matching circuit and the DC-DC converter-type power supply which can perform energy recovery and envelope tracking function. Also the paper suggests the detailed circuit topology. Its characteristics are verified by the detailed experimental results.

Recently according to the development of DC transmission which is high efficiency, stability of DC grid has become a major concern. Therefore, the reliable DC SSCB (Solid-State Circuit Breaker) is required in order to secure high safety. The previous DC SSCB is unstable and many elements are required in order to perform the operating duty. This paper proposes a new DC SSCB. Since the proposed DC SSCB does not cause delay of breaking operation caused by charging operation, it can break an accident quickly. Also, since it can perform the operation duty with a few elements, it is economical. The operating characteristic of the proposed DC SSCB is verified by simulations on short circuit fault. Also, this paper suggests the design guidelines so that it can be applied to any other DC grid.