Earticle

본 논문은 정확한 위치인식 및 차세대 측위 기술로써 각광 받고 있는 기술인 IR-UWB(Impulse-Radio Ultra Wideband) Radar를활용하여 근거리 환경에서 정확한 목표물을 추정하고자 3가지 기존 반사파 제거 알고리즘과 하나의 제안하는 알고리즘을 사용하였다. 수신된 신호에는 목표물 신호 이외에 불필요한 반사파 신호들이 존재한다. 불필요한 신호들을 제거하고 정확한 목표물 신호만을추정하기 반사파 제거 알고리즘을 사용하였다. 하나의 송신 안테나와 수신안테나로 목표물들의 위치를 실시간으로 추정하였다. 기존의 3가지 반사파 제거 알고리즘의 단점들을 보완하고자 새로운 반사파 제거 알고리즘을 제안하여 가장 정확한 목표물을 추정하였다. 또한 차량 등 외부 탑재를 위해 DSP(Digital Signal Processor)로 변환을 하였다. 본 논문은 향후 진행 되어질 반사파 제거 연구에도움이 될 것이라 생각한다.

In this paper we propose three existing reflection removal algorithms and one proposed algorithm to estimate accurate targets in near field using IR-UWB (Impulse-Radio Ultra Wideband) radar. The received signal includes unnecessary reflected wave signals to the target signal. A reflective cancellation algorithm was used to remove unnecessary signals and estimate only the correct target signal. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. In order to overcome the disadvantages of the existing three reflection removal algorithms, we propose a new reflection removal algorithm and estimate the most accurate target. Also we used DSP(Digital Signal Processor) to install the external mounting of vehicles. This paper will contribute to the study of the future reflections.

본 논문은 저 비용, 저 소비전력으로 실내외에서 수십 cm급의 정밀한 위치인식이 가능한 IR-UWB(Impulse-Radio Ultra Wideband) Radar 시스템에서 정확한 목표물을 추정하고자 Kalman 필터 및 RRWA 알고리즘을 사용하였다. 목표물에 의해 반사된 신호에는 목표물 신호이외에 불필요한 신호들이 존재한다. 불필요한 신호들을 제거하고 정확한 목표물 신호를 도출 및 성능 향상을 위해 노력 하였다. 하나의 송신 안테나와 수신안테나로 목표물들의 위치를 실시간으로 추정한다. Kalman 필터를 사용하여 배경잡음을 제거하 였고 RRWA 알고리즘을 사용하여 반사파 신호를 제거하여 기존의 목표물 신호를 추정했을 때보다 더 좋은 성능을 보였다. 본 논문 에서는 향후 진행 되어질 목표물 추정에 있어 정확한 거리 추정 및 추적을 하는 연구에 도움이 될 것이라 생각한다.

In this paper, Kalman filter and RRWA algorithm are used to estimate the accurate target in IR-UWB (Impulse-Radio Ultra Wideband) radar system, which enables accurate location recognition of indoors and outdoors with low cost and low power consumption. In the signal reflected by the target, unnecessary signals exist in addition to the target signal. We have tried to remove unnecessary signals and to derive accurate target signals and improve performance. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The Kalman filter was used to remove the background noise and the RRWA algorithm was used to remove the reflected signal. In this paper, we think that it will be useful to study the accurate distance estimation and tracking in future target estimation.

본 논문은 투과성이 좋고 실내 환경에 강건하며, 수십 센티미터(cm) 급의 고정밀 측위가 가능하다는 점에서 주목 받고 있는 IR-UWB(Impulse-Radio Ultra Wideband) 레이더 시스템에서 신호의 특성을 이용한 적응형 다중 목표물 추정 알고리즘을 제안한다. 목표물에 의해 반사되는 신호는 Peak를 갖는 다는 특성으로 다중의 Peak를 추정하는 알고리즘을 제안하였다. 이러한 알고리즘의 성능을 확인하기 위해서 레이더 앞에 다중 목표물을 두고 기존의 기법과 다중 목표물 추정 알고리즘을 비교하였다. 하나의 송신 안테나와 수신안테나로 목표물들의 위치를 실시간으로 추정한다. 기존의 최고 신호 도출 방식에 비해 추정할 수 있는 수가 늘어나고 다중으로 목표물 도출이 가능하다. 기존의 기법은 하나의 목표물만 추정하다보니 평균 제곱 오차가 1이 나오는 반면 다중 목표물 추정 알고리즘은 약 0.05의 결과가 도출된다. 본 논문에서 제시한 기법은 하나의 IR-UWB 모듈 환경에서 다중의 목표물을 추정 및 응용에 적용할 수 있을 것이라 기대된다.

In this paper, we propose an adaptive multi-target estimation algorithm using the characteristics of signals in the IR-UWB(Impulse-Radio Ultra Wideband) radar system, which is attracting attention because it has good transparency, robustness to the indoor environment, and high precision positioning of tens of centimeters. We proposed an algorithm that estimates multiple peaks with the characteristic that the signal reflected by the target has a peak. To verify the performance of these algorithms, multiple targets were placed in front of the radar and the existing technique and the multi - target estimation algorithm were compared. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The number of estimates can be increased compared with the existing peak signal derivation method, and multiple targets can be derived. The conventional technique estimates only one target, which results in a mean square error of 1 while a multi - target estimation algorithm yields a result of about 0.05. The proposed method is expected to be able to apply multiple targets to the estimation and application in one IR-UWB module environment.

The purpose of this study is to prove the impact relationship between the recognition, attitude and visit intention of 2018 Travel week. In addition, It analyzed the differences in the recognition, attitude and visit intention according to demographic characteristics. Through this measure, It aims to raise recognition of the travel week and reflect it in related policies to ultimately promote the revitalization of domestic tourism. The study was conducted for 10 days from Oct. 1 to 10, 2018 among citizens nationwide at five survey sites. The statistical analysis of the data was conducted using techniques of regression analysis and analysis of variance(ANOVA). As a result, it was analyzed that the recognition of travel week affects attitude and visit intention. In other words, increasing the recognition of the travel week results in an increase in attitude and visit intention. There were differences in three categories by age and some differences by gender and income. There was no difference by educational background. Based on the results of this analysis, I would like to suggest some suggestions for revitalizing domestic travel by raising recognition of travel week. First, the main slogan should be selected for a clear travel week. Second, the promotion should be diversified to expand public participation. Third, it is necessary to discover travel week goods that can be an issue. Finally, strategic promotional marketing should be done.

To improve the recognition rate of dynamic target under complex background, this paper studied the target characteristics and recognition algorithm of moving target. According to the light intensity, target shape and motion characteristics, the calculation model of the target characteristics was established, the optical radiation characteristic and the detection performance of photoelectric detection system under complex background were analyzed. By means of image segmentation and mathematical morphology transform, the target information extraction and recognition algorithm was researched and analyzed. Through the simulation calculation and experimental verification, the results show that target characteristics model and recognition algorithm is feasible.

In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its application in image analysis and pattern recognition, due to its good characteristics of high recognition rate, robustness to corruption and occlusion, and little dependence on the features selection etc. However, sufficient training samples are always required by the sparse representation method for the effective recognition. In practical applications, it is generally difficult to obtain sufficient training samples of the test targets, especially non-cooperative targets. So the key issues in the effective automatic target recognition (ATR) based on the sparse representation are to obtain sufficient training samples in different scales, angles, and different illumination conditions, and to construct an overcomplete dictionary with discriminative ability. In this paper, a novel sparse representation-based scheme is proposed for the automatic target recognition in the real environment, in which the training samples are drawn from the simulation models of real targets and the overcomplete dictionary is trained using structured sparse learning method. The experimental results show that the proposed method is effective for the automatic target recognition in the practical application, especially, where the desired features of the sparse representation method are kept.

The aim of this paper is to propose a new multi-sensor target recognition method base on relative entropy evaluation theory. There are several influencing factors in the target recognition problem, which needs several sensors to work together. Then the multi-sensor target recognition problem can be regarded as a multi-attribute decision making problem. Relative entropy measure can depict the closeness of the two systems, and then this paper will use it to develop an improved TOPSIS method for the multi-sensor target recognition problem. A new characteristic index weights method is proposed, which can avoid the subjectivity of the weight of characteristic indexes. Finally, an application example is used to illustrate the effectiveness and feasibility of the proposed method.

Traditional radar target recognition algorithms utilize balance data set to train the classifier and achieve a satisfactory result on a balance test data set. However, in the case of non-cooperative target recognition, we only obtain a small amount of non-cooperative target samples, while we can obtain a larger number of cooperative target samples easily, which leads to an imbalance training data set. In this paper, we consider the imbalance data classification problem in radar target recognition. We utilize the cost-sensitive approach and assume that different kinds of mistakes lead to different losses. Based on this assumption, a novel radar target recognition algorithm, called isometric cost-sensitive Laplacian eigenmaps (ICSLE), is presented. The basic idea of ICSLE is that the larger the misclassification cost is, the further the distance between two classes is, and vice versa. Moreover, in order to effectively utilize the cost information and local property of observation samples, we use the geodesic distance as the edge weight, instead of the local Euclidean distance. Experiments on millimeter wave radar high-resolution range profile (HRRP) demonstrate the effectiveness of our method.

Aiming to the traditional acoustic detection array with measuring the target coordinate, the accuracy of measurement data is affected by the propagation velocity of the acoustic pressure. This paper proposes a new measurement method, which obtain the mathematical model of the target parameter information when the target passing through acoustic sensor array based on acoustic array structure establish. From the model the target parameter information is just associated with the respective probe element’s delay time of the acoustic pressure when the target passing through the acoustic detector, thereby it is effective to avoid the measurement accuracy influence from acoustic pressure velocity of the target parameter information. In order to obtaining the signal characteristics when the target passing through acoustic detector array, it would adopt time-frequency transform method to analyzed signals of the target through the acoustic array. By the simulation experiment it can improve the measurement precision of the target parameters information, and get frequency-domain characteristics of target information based on the measured analog signals of acoustic pressure. The result is benefit to enhance precision of target parameter information from acoustic array detection system, and it has certain guiding significance for the study of acoustic array structure.

In view of the greater changes of posture, illumination, expression and scene in reality environment have a strong impact on wild face recognition algorithm to identify performance problem, and puts forward a kind of linear discriminant analysis side information (SILD) algorithm on hyperplane fusion of learning prototype. First of all, using support vector machine (SVM) to weak tag of data-concentrated sample is expressed as the middle-level characteristics of prototype hyperplane, using a learning combination coefficient to select sparse support vector set from untagged conventional data set; then, under the constraints of the combination sparse coefficient of SVM model, by using Fisher linear discriminant criterion to maximize discriminant ability of untagged data set, and using the iterative optimization algorithm to solve the objective function; in the end, using SILD for feature extraction, cosine similarity measure to complete the final face recognition. In two general face data sets of wild face recognition (LFW) and YouTube, it makes comparison of PHL+SILD method and low-level features + SILD method on some characteristics, such as strength, LBP, Gabor feature and Block Gabor feature, average accuracy, area under the curve (AUC) and entire error rate (EER). The validity and reliability of the proposed algorithm is verified by the experiments.

cDNAs encoding three isoforms of OGT were expressed in Escherichia coli and the co-expression system of OGT with target substrates was established in vivo. No endogenous bacterial proteins were significantly O-GlcNAcylated by any type of OGT isoforms while the co-expressed target proteins were strongly and differentially O-GlcNAcylated depending on OGT isoforms. We found that sOGT was auto-O-GlcNAcylated whereas ncOGT and mOGT were not. Under these conditions, more importantly, sp1 can interact with ncOGT by which recognition of target protein byOGT is newly formulated. These results strongly suggest that sp1 may modulate OGT activity of target recognition.

Moving object tracking technique is to find the exact location of the target in the next frame, and feedback to a tracking system for tracking and to provide an important basis and foundation for the analysis and understanding of the video sequence. Face recognition refers to the method to extract somehow be able to describe the characteristics of each individual's personality. Using 2DPCA image feature extraction, feature dimension reduction is simpler and direct, so the calculation efficiency is relatively high, and it can greatly shorten the training time of the sample collection of images. This method is the first application the 2DPCA optimal representation of characteristics for the original sample matrix, and then apply FLDA optimal discriminate feature for the original sample. The paper proposes the method of moving target tracking based on 2DPCA and FLDA face recognition algorithm.

본 논문에서는 밀리미터파대역의 공대지 레이다에서 이중 편파 채널을 활용한 지상 표적을 식별하기 위한 방법을 제안한다. 먼저 공대지 레이다의 조우 상황에서 Push-Broom 표적 탐지 방법을 설명하고 수신 신호를 모델링한다. 시간 영역 스펙트럼 추정 기법인 RELAX 알고리즘을 이용하여 산란점을 추출하고 표적의 특성 벡터를 생성하였다. 그리고 이를 기반으 로 각각의 4표적에 대한 DB를 구성하였다. 제안하는 방법으로 표적 식별 시뮬레이션을 수행한 결과 이중 편파 채널의 데이 터를 이용하면 단일 채널에 비해서 표적 식별률이 최대 15% 이상 높아지는 것을 확인할 수 있었다.

In this paper, we propose a method for recognizing ground target using dual polarization channels in millimeter waveband air-to-surface radar. First, the Push-Broom target detection method is described and the received signal is modeled considering flight-path scenario of air-to-surface radar. The scattering centers were extracted using the RELAX algorithm, which is a time domain spectral estimation technique, and the feature vector of the target was generated. Based on this, a DB for 4 targets is constructed. As a result of the proposed method, it is confirmed that the target classification rates is improved by more than 15% than the single channel using the data of the dual polarization channel.

MicroRNAs (miRNAs) are small non-coding RNAs (~22 nucleotides) regulating gene expression at the post-transcriptional level. By directing the RNA-induced silencing complex (RISC) to bind specific target mRNAs, miRNA can repress target genes and affect various biological phenotypes. Functional miRNA target recognition is known to majorly attribute specificity to consecutive pairing with seed region (position 2-8) of miRNA. Recent advances in a transcriptome-wide method of mapping miRNA binding sites (Ago HITS-CLIP) elucidated that a large portion of miRNA-target interactions in vivo are mediated not only through the canonical "seed sites" but also via non-canonical sites (~15-80%), setting the stage to expand and determine their properties. Here we focus on recent findings from transcriptome-wide non-canonical miRNA-target interactions, specifically regarding "nucleation bulges" and "seed-like motifs". We also discuss insights from Ago HITS-CLIP data alongside structural and biochemical studies, which highlight putative mechanisms of miRNA target recognition, and the biological significance of these non-canonical sites mediating marginal repression.

In this paper, we present a three-dimensional (3-D) automatic target recognition system based on optical integral imaging reconstruction. In integral imaging, elemental images of the reference and target 3-D objects are obtained through a lenslet array or a camera array. Then, reconstructed 3-D images at various reconstruction depths can be optically generated on the output plane by back-projecting these elemental images onto a display panel. 3-D automatic target recognition can be implemented using computational integral imaging reconstruction and digital nonlinear correlation filters. However, these methods require non-trivial computation time for reconstruction and recognition. Instead, we implement 3-D automatic target recognition using optical cross-correlation between the reconstructed 3-D reference and target images at the same reconstruction depth. Our method depends on an all-optical structure to realize a real-time 3-D automatic target recognition system. In addition, we use a nonlinear correlation filter to improve recognition performance. To prove our proposed method, we carry out the optical experiments and report recognition results.

Various kinds of systems, that can do target recognition and position detection simultaneously by using infrared sensing detectors, have been developed. In this paper, the detection system TRSS (Thermal target Recognition by Spiral Scanning) adopts linear array shaped uncooled IR detector and uses spiral type fast scanning method for relative position detection of target objects, which radiate an IR region wavelength spectrum. It can detect thermal energy radiating from a 9 m-size target object as far as 200 m distance. And the maximum field of a detector is fully filled with the same size of target object at the minimum approaching distance 50 m. We investigate two types of lens systems. One is a singlet lens and the other is a doublet lens system. Every system includes one aspheric surface and free positioned aperture stop. Many designs of F/1.5 system with ${\pm}5.2^{\circ}$ field at the Efl=20, 30 mm conditions for single element and double elements lens system respectively are compared in their resolution performance [MTF] according to the aspheric surface and stop position changing on their optimization process. Optimum design is established including mechanical boundary conditions and manufacturing considerations.

This paper discusses a photon-counting linear discriminant analysis (LDA) with computational integral imaging (II). The computational II method reconstructs three-dimensional (3D) objects on the reconstruction planes located at arbitrary depth-levels. A maximum likelihood estimation (MLE) can be used to estimate the Poisson parameters of photon counts in the reconstruction space. The photon-counting LDA combined with the computational II method is developed in order to classify partially occluded objects with photon-limited images. Unknown targets are classified with the estimated Poisson parameters while reconstructed irradiance images are trained. It is shown that a low number of photons are sufficient to classify occluded objects with the proposed method.

Xo2276 is a putative transcription activator-like effector (TALE) in Xanthomonas oryzae pv. oryzae (Xoo). Xo2276 was expressed with a TAP-tag at the C-terminus in Xoo cells to enable quantitative analysis of protein expression and secretion. Nearly all TAP-tagged Xo2276 existed in an insoluble form; addition of rice leaf extracts from a Xoosusceptible rice cultivar, Milyang23, significantly stimulated secretion of TAP-tagged Xo2276 into the medium. In a T3SS-defective Xoo mutant strain, secretion of TAPtagged Xo2276 was blocked. Xo2276 is a Xoo ortholog of Xanthomonas campestris pv. vesicatoria (Xcv) AvrBs3 and contains a conserved DNA-binding domain (DBD), which includes 19.5 tandem repeats of 34 amino acids. Xo2276- DBD was expressed in E. coli and purified. Direct in vitro recognition of Xo2276-DBD on a putative target DNA sequence was confirmed using an electrophoretic mobility shift assay. This is the first study measuring the homologous expression and secretion of Xo2276 in vitro using rice leaf extract and its direct in vitro binding to the specific target DNA sequence.

Traumatic brain injury (TBI) is a critical cause of disability and death worldwide. Many studies have been conducted aimed at achieving favorable neurologic outcomes by reducing secondary brain injury in TBI patients. However, ground-breaking outcomes are still insufficient so far. Because mild-to-moderate hypothermia (32°C?35°C) has been confirmed to help neurological recovery for recovered patients after circulatory arrest, it has been recognized as a major neuroprotective treatment plan for TBI patients. Thereafter, many clinical studies about the effect of therapeutic hypothermia (TH) on severe TBI have been conducted. However, efficacy and safety have not been demonstrated in many large-scale randomized controlled studies. Rather, some studies have demonstrated an increase in mortality rate due to complications such as pneumonia, so it is not highly recommended for severe TBI patients. Recently, some studies have shown results suggesting TH may help reperfusion/ischemic injury prevention after surgery in the case of mass lesions, such as acute subdural hematoma, and it has also been shown to be effective in intracranial pressure control. In conclusion, TH is still at the center of neuroprotective therapeutic studies regarding TBI. If proper measures can be taken to mitigate the many adverse events that may occur during the course of treatment, more positive efficacy can be confirmed. In this review, we look into adverse events that may occur during the process of the induction, maintenance, and rewarming of targeted temperature management and consider ways to prevent and address them.