Earticle

본 연구는 초등학교 교육 현장에서 효 교육의 한 방법으로 효 교육의 지역화 방안을 찾는데 관심을 갖고 진행되었다. 효 교육의 지역화는 학생들에게 자기가 속한 고장의 효 관련 자료를 교육내용으로 선정 조직하여 학습함으로써 올바른 인성을 갖게 하고 자신의 정체성 확인과 함께 자율성과 창의성을 갖게하는 과정이라 할 수 있다. 효 교육의 지역화는 다양한 방법으로 활용 할 수 있으며 읽기 자료화 내가 닮고 싶은 효행인물 찾기 효행인물 따라 실천하기 효행 인물 이야기 읽고 감상문 쓰기 효 실천 내용 그리기 등의 방법을 들 수 있다. 지역화 된 효 교육은 효 교육 방법을 다양화하고 학습자가 속한 지역사회를 바탕으로 한다는 점에서 친밀감과 관심을 갖고 자신이 속한 지역 문화를 배경으로 실천하며 지역인이 함께 지도하고 학습하며 참여 할 수 있는 프로그램으로 지역의 효 문화 수준 발전과 효를 실천하는 지역사회를 만드는 한 방법이 될 것이다.

In this study, a universal gated recurrent unit (u-GRU)-based location estimation (LE) method is proposed to obtain the location of an ultra-wideband (UWB) transmitter. The proposed u-GRU-LE system consists of a GRU-based classifier and nine localizers for nine channel models (CMs). The classifier first predicts the CM, and then, the proper localizer is selected according to the predicted CM to estimate the location of the transmitter. Rigorous simulations are executed with various CMs. From the results, it is verified that the proposed universal GRU-based 3D localization method generally performs well irrespective of the channel environments.

Real Time Location System (RTLS) refers to a system that provides various services by measuring location information of objects in real time. The RTLS system is being used in many fields related to the Internet of Things (IoT), such as medical, healthcare, performances, and production facilities. A high-accuracy positioning system is essential for quality of RTLS. A variety of methods such as triangulation, trilateration, and MDS are utilized for positioning, but each has its own drawbacks. We propose an efficient and accurate advanced positioning system with Deep Reinforcement Learning (DRL). In the learning environment, we adopt the Proximal Policy Optimization (PPO) algorithm and Adam Optimizer. The proposed system estimates the exact position with a small amount of computation using only distance information from four anchor nodes in a 3D environment. Through system performance evaluation, we proved that the proposed system showed superior performance compared to the existing system.

In this paper, we propose a new framework that enables an object detector trained with only point-level annotations to estimate the centroids and sizes of objects in dense scenes. Specifically, the framework is based on the Swin Transformer structure and introduces a self-designed resolution feature fusion module in the hierarchical structure, where the estimation of object centroids is done directly by point supervision, and the object pseudo-size is initialized based on the assumption of local uniform distribution, and the regression of object size is guided by an improved congestion-aware loss function. In the NWPU-Crowd dataset, our method outperformed the existing state-of-the-art detection counting methods in F1-measure, precision, MSE evaluation criteria.

Magnetic Sensors can be employed to localize the unmanned vehicle which is running a predefined path where magnets are embedded for certain spaces. Among various sensor types, sensor arrays of 1-dimensional magnetic sensor have the merit of easy elimination of external magnetic component such as terrestrial magnetism. However, interpolation should be considered in the array sensors in order to increase the precision level because there is a limit in arranging sensors in close interval. We propose the novel interpolation method which can be performed with simple computation and represents the improved accuracy by increasing the linearity of the interaction formula. Demonstration of the linearity and simulation results show the proposed method exhibits the improved accuracy compared to the conventional method.

자율 주행 기술이 발전함에 따라 주행 주변 환경을 인식하는 데 차량 위치의 정확성은 매우 중요하다. 측위의 정확도를 높이기 위해 정밀지도를 사용한 지도 정합 측위기술(map-matching localization)이 연구되고 있다. 기존의 지도 정합 기법은 지도에서 차선의 중심으로 표현된 데이 터를 기반으로 차량 위치를 추정하기에 차선 내 측면 거리의 편차를 반영하지 않는다. 따라서 본 논문에서는 정밀한 측위를 제공하기 위해 영상처리를 통한 차선 검출 기법과 정밀지도의 차선 위치 정보를 이용한 기법을 제안한다. 영상 처리 기법으로 IPM(inverse perspective mapping)과 다중 차선 검출 기법, 중앙선 검출 기법을 통하여 차선 번호를 검출하고 차선 이탈 감지 방법으로 차선 중심으로부터 차량의 측면 거리를 추정한다. 최종적으로 영상처리로 검출 한 차선 번호와 GNSS / INS의 위치를 기반으로 정밀지도에서 위치 링크정보를 추출하고 추출 된 링크에 측면 거리를 반영하여 차선 내 차량의 위치를 추정한다. 제안된 방법의 성능을 평가 하기 위하여 실제 도로에서 실험하였다. 제안하는 방법은 GNSS / INS와 비교 시 약 1.0m 정도 정확도가 개선되며, 기존의 차선레벨 맵매칭 방법과 비교 시 구간별로 약 0.04m ~ 0.21m (7~30%) 정확도가 개선됨을 확인하였다.

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m～0.21m (7～30%) for each section when compared with the existing lane-level map matching method.

Internet of Things (Internet of Things, IoT) is becoming commonplace at a faster rate, thus the study of low-power long-distance communication is being accelerated. In addition, localization based on wireless sensor network bas been interested. Positioning using LoRaWAN boasts relatively excellent accuracy compared to other technologies, but it seems a lot of error in the nature of other wireless technologies in indoor environment. This paper correct TDOA localization result using in advance constructed TDOA correction database in such indoor positioning.

스마트폰 기반의 추적은 다양한 시나리오에서 널리 연구되어왔다. 그러나 우리가 아는 한, GPS 신호 및 무선 인프라가 항상 이용 가능하지 않는 지하 대중교통 시스템의 추적 문제를 해결한 연구는 극소수에 불과하다. 전동차의 위치를 아는 것은 지하철 승객에게 유용한 서비스를 개발하는데 필요하다. 따라서 지하철 승객을 대상으로 스마트폰 센서를 이용하여 모션 상태 및 정차역을 추정하는 방법이 연구되고 있다. 이 논문에서는 스마트폰의 기압계와 자기 센서 를 이용하는 추적 방법을 제안한다. 이 논문에서 제안된 방법은 먼저 기압의 변동 및 자기장의 세기에 따라 전동차가 운행하는지 정차하는지를 추정한다. 그 다음에 고도 값과 자기장 값으로 전동차가 정차하는 정확한 역을 추정한다. 우리 는 서울의 지하철 5호선 데이터를 사용하여 제안된 방법을 평가했다. 이전 방법과 비교하여 제안된 방법은 더 높은 정확 성을 얻었다.

Smartphone-based localization has been widely studied in many different scenarios. But as far as we know, few work has addressed the problem of localization in underground public transportation systems, where GPS signal and wireless infrastructure are not always available. Knowing the location of a train is necessary to develop a useful service for subway passengers. And so, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses a barometer and a magnetic sensor on a smartphone. The method proposed in this paper first estimates whether the train runs or stops according to the change in air pressure and the strength of the magnetic field. The altitude value and the magnetic field value are then used to estimate the exact stop station of the train. We evaluated the proposed method using data from the Seoul’s subway line 5. Compared with previous methods, the proposed method achieves higher accuracy.

사물 인터넷을 구성하는 센서 네트워크는 주변 환경의 여러 정보들을 효율적으로 수집하여 앵커 노드로 보 내기 위해서 최적의 경로를 선택하여 센서 간의 통신비용 및 수명 연장을 위해 많은 연구가 필요하다. 센서 네트워 크에서 센서노드는 저전력, 저용량을 갖는 확장 장치이다. 이들 노드들 간의 데이터 전송을 위한 라우팅 방법을 위 해 인접한 지역에 있는 앵커와 노드간의 연결은 적은 오류로 비교적 정확해야 한다. 이러한 지역화를 위한 기존의 방법 중 CA(Centroid Algorithm)는 자주 사용하는 방법이기는 하지만 오류가 자주 일어난다. 본 논문에서는 이러한 오류 발생의 문제점을 최소화하기 위해 Radical Line을 기반으로 한 센서 노드 간에 range-free 지역화 방법을 제안한다 .

The sensor network that is component of the Internet of Things require a lot of research to select the best route to send information to the anchor node, to collect a number of environment and cost efficient for communication between the sensor life. On the sensor network in one of the components of IOT's environment, sensor nodes are an extension device with low power low capacity. For routing method for data transmission between the sensor nodes, the connection between the anchor and the node must be accurate with in adjacent areas relatively. Localization CA (Centroid Algorithm) is often used although an error frequently occurs. In this paper, we propose a range-free localization method between sensor nodes based on the Radical Line in order to solve this problem.

현재 우크라이나-러시아 전쟁을 기점으로 국산 방산제품의 해외수출이 급격하게 탄력을 받고 있다. 국산 방산제품의 대외 경쟁력을 지속적으로 개선할 수 있는 방법은 실질적인 국산화 고도화를 촉진하는 핵심도구로써 국산화 평가방안을 활용하는 방안이라고 판단된다. 연구 목적은 국산화 수준의 지속적인 고도화를 유인할 수 있도록 국산화 평가방안을 개선함으로써 방위산업분야의 일하는 방식을 혁신하는데 작은 보탬이 되는 것이다. 연구 범위는 첫째, 현행 국산화 평가의 한계를 파악하고자 한다. 둘째, 국산화 평가가 왜곡되었을 때 전체 수명주기에의 영향을 분석한다. 셋째, 최종적으로 국산화를 실질적으로 수행할 수 있는 발전방안을 모색한다. 연구 방법은 질적 연구방법론을 적용한다. 1차와 2차 델파이 조사, 심층 인터뷰를 통해 다양한 이해집단의 의견을 수집하 여 분석한다. 상호 대립되는 의견은 심층 인터뷰의 전문가 토론을 통해서 합의를 도출한다. 연구를 수행한 결과, 현행 국산화율 산정 방식은 악용되거나 왜곡의 가능성이 높은 것으로 분석되었다. 이 문제를 개선하 기 위해서는 국산화율 산정 대상이 되는 협력업체를 2차 혹은 3차로 확대할 필요가 있다고 합의가 도출되었다. 국산화율 산 정 결과가 왜곡되면 그 영향은 전체 수명주기가 진행됨에 따라 가중된다고 부분적으로 확인하였다. 그러나 운영유지단계의 국산화율의 왜곡 영향을 줄이기 위해 먼 미래의 운용유지 비용을 현재가치로 환산하여 국산화율에 반영하여 평가하는 것은 또 다른 왜곡 가능성을 야기할 수 있으므로 타당하지 않은 것으로 확인하였다. 연구 결론을 정리하면 현행 국산화 평가는 국산화율 산정이라는 단순한 비용 성과의 측정에만 의존하고 있고, 국산화율 산 정 범위를 1차 협력업체에 제한함으로 인해 악용이나 왜곡의 가능성이 높다는 한계가 있다. 이를 개선하기 위해서는 궁극적 으로 비용 성과 뿐만 아니라 다양한 성과를 종합적으로 평가하는 모델이 필요하지만, 단기적으로는 현행 비용 측면의 성과 측정 방식은 유지하되 국산화율 산정 범위를 확대하는 것이 현실적이다. 본 연구의 기대 효과는 국산화 평가방안의 개선에 대한 필요성을 제기하여 국산화를 지속적으로 촉진할 수 있는 국산화 평가방안을 모색하는 것이고, 더 나아가서는 국산 방산물자의 대외경쟁력을 확보하기 위한 핵심 수단으로 국산화 평가를 활 용되는 것이다. 또한 방위사업분야에는 다양한 이해관계자들이 서로 대립되는 의견이 여러가지 분야에서 나타날 수 있는데 질적 연구방법론을 도입하면 상호 간의 다양한 관점의 공유, 토론 및 합의를 통한 해결방안 도출에 많은 도움이 될 수 있다.

Currently, overseas exports of domestic defense products are gaining momentum starting with the Ukraine-Russia war. The way to continuously improve the competitiveness of domestic defense products is to utilize localization evaluation measures as a key tool to promote substantial localization advancement. This study aims to contribute to innovation in the way of working in the defense industry by inducing the level of localization to continuously upgrade by improving the evaluation plan for localization. First, the scope of the study is to understand the limitations of the current localization evaluation. Second, the effect on the total life cycle is analyzed when the localization evaluation is distorted. Third, finally, it seeks development solitions that can practically carry out localization. Qualitative research methodology is applied for the effective performance of this study. The opinions of various interest groups are collected and analyzed through the f irst and second Delphi surveys and in-depth interviews. Conflicting opinions are agreed through expert discussions in in-depth interviews. As a result of the performance of this study, it was analyzed that the current localization rate calculation method has a high possibility of abuse or distortion. In order to solve this problem, an agreement was reached that it was necessary to expand the scope of calculation to the 1st or 2nd partner companies. It was partially confirmed that when the result of calculating the localization rate is distorted, the effect is increased as the total life cycle progresses. However, in order to reduce the distortion effect of the localization rate at the operation maintenance stage, it was confirmed that it is not reasonable to convert the operational maintenance cost into the present value and reflect it in the localization rate. In summary, the current localization evaluation relies only on measuring the simple cost performance of calculating the localization rate, and there is a limitation that the possibility of abuse or distortion is high due to the limitation of the scope of calculating the localization rate to primary suppliers. In order to improve this, a model that comprehensively evaluates not only cost performance but also various performance is needed, but in the short term, it is realistic to maintain the current cost-related performance measurement method and expand the scope of localization rate calculation. The expected effect of this study is to seek ways to continuously promote localization by raising the need for improvement of localization evaluation measures, and furthermore, localization evaluation is used as a key tool to secure the external competitiveness of domestic defense products. In addition, various stakeholders may have conflicting opinions in various fields in the defense sector, and the introduction of qualitative research methodology can help each other share various perspectives and come up with solutions through discussions and agreements.

비디오에서 행동의 위치를 추정하기 위해 행동의 시작과 끝에 모두 주석을 다는 것은 많은 비용을 요구한다. 따라서 주석 생성 비용을 줄이기 위해 비디오 단위의 행동 카테고리만 존재하는 약지도 학습이 활발히 진행되고 있지만 약 지도 학습은 행동단위 주석의 부재에 따른 많은 한계가 존재한다. 최근에는 주석의 생성 비용은 줄이면서 약지도 학 습에 비해 훨씬 개선된 성능을 낼 수 있는 포인트 주석 기반 약지도 학습에 대한 연구가 진행되고 있다. 하지만 이는 행동구간 내의 포인트가 존재하는 위치에 많이 의존한다는 한계가 있다. 본 연구진은 이런 점을 보완하고자, 행동구 간을 나타낼 수 있는 확률분포를 설계하고 포인트 주석을 샘플링하여 학습이 진행됨에 따라 점차 포인트 주석을 행 동구간의 가운데 방향으로 움직일 수 있게끔 하는 포인트 업데이트 방식을 제안한다. 실험은 제안하는 방법이 기존 의 포인트 주석 기반 학습에서 치우친 포인트가 주어진 경우 성능의 한계를 개선함을 보여준다.

Labeling the starting and ending points of interesting actions in a video is labor-intensive and expensive. To reduce the labeling costs, many studies have been conducted on weakly supervised learning, where only video-level action labels exist, but it has limitations due to the absence of frame-level annotations. Recently, research has been conducted on point annotation-based learning, which can achieve much improved performance while reducing labeling costs. However, this has a limitation in that the performance depends the location of annotated points in the action interval. To compensate for this, we design a probability distribution that can represent action intervals, which allows us to sample point annotations and update them to gradually move point annotations toward the center of action intervals as learning progresses. Experiments show that the proposed method improves the performance limitation given biased points in a point annotation-based learning scheme.

Software defects are the major risks of system stable operation. Its error localization technology of automation is one of the key research content for trust computing and software assurance. In this paper, we have proposed a new model, which integrates the current methods by analyzing the program structure. We put forward a new automated fault localization method, this method without the degree loss of automation, at the same time, particle size down to basic positioning code statements, makes the position more accurately. We have designed an experiment to examine the effectiveness of CPD base on SIR. And we proved that the CPD is more effective.

Most of the localization algorithms have performed localization utilizing absolute point-to-point distance estimates. If the sensor network is not cooperative, there’s no information about the strength of the original signal so that the location of a target node is hard to be found. It allows ratiometric location algorithm to be proposed. GPS algorithm give accurate location of unknown node, however it needs the absolute distance between the nodes. Ratiometric GPS iteration (RGPSi) localization method combines GPS and ratiometric algorithm for utilizing the benefits of two algorithms. RGPSi algorithm results in a little more position error and longer iteration than GPS algorithm. We introduce the method that helps ratiometric GPS iteration algorithm to be more accurate and reduce the iteration numbers through the angle of arrival localization algorithm. Not to add computation complexity to the RGPSi algorithm, nonlinear angle to location relationship is made linear. The proposed RGPSi combined with AOA algorithm shows more accurate position errors and less iteration numbers.

Wireless sensor networks receive much attention in these few years due to its wide spectrum of applications. Localization is one of the significant techniques in the ubiquitous sensor networks, and most localization techniques nowadays apply RSSI-based ranging techniques to compute the location of the object in wireless sensor network. However, a wireless sensor network is of a fading-signal environment that comprises noises, and the noises cause RSSI to become unstable and lead to abrupt distance estimates. In this paper, we propose Verification-Based Localization Method (VBLM) to alleviate the effect of the unstable signal and provide the high-accuracy location estimates in wireless sensor environments. The basic idea of VBLM is to prune noisy signals in the localization process. In our method, the sensor node would use another neighboring beacon to assist to verify the quality of received signal under acceptable communication cost, and thus, the noisy signals can be removed to avoid increasing the error in the localization. The experimental results show that VBLM indeed reduces the localization error in the unstable signal sensor networks than other localization methods.

The methods of the fault diagnosis and degradations used in the different industrial sectors are various and consider the specifity of the materials forming their industrial processes. For some relatively simple processes, the relations between the causes and their effects are Biunivocal and the diagnosis with reverse reasoning is easy. Contrarily for the complexes processes, the situation is slightly different and is impossible to precede a deductive reasoning. The diagnosis is therefore only possible by using a different and a complementary knowledge. The sensitivity method for the detection and localization of the linear system defects is used in this paper.

In recent years, smart homes have become the center of interest for IT companies and construction companies and various types of smart homes have been made currently available on the market. Yet, these equipment are costly and it is not easy to convert existing equipment for smart home application as they may require additional resources which could also inflict much costs. The extra costs involving the remodeling of existing housing structure and installment of new equipments can be avoided by using advanced wireless technologies. As an example, this paper proposes an indoor localization system that adopts Bluetooth technology and uses RSSI values for localization. Researchers have configured a system where the central control device will recognize all other devices or equipments in the system, communicate with each other, and respond to the commands or the information provided. However, despite the efforts of many researchers, existing RSSI-based indoor localization systems do not show a satisfactory level of accuracy such that we have devised a system that traces the trend in the RSSI samples. The RSSI sampling algorithm uses Delta values obtained from the Delta sampling process to improve system accuracy and to lower the costs. The analysis results led us to believe that our algorithm has a reduced localization error rate by 12%-point compared to the algorithm that used raw sampling method.

In order to reduce localization error of the sensor node, a novel node localization algorithm (DVHop-Steff) is proposed in this paper based on DV-Hop algorithm and Steffensen iterative method. Firstly, DV-Hop algorithm is used to obtain location position of wireless sensor nodes; and then Steffensen iterative method is used to correct the locate results of DV-Hop algorithm and achieve higher localization precision, finally, the performance of the proposed algorithm is tested by simulation analysis. The results show that the localization precision of the proposed algorithm has been improved significantly and has more wide application scope.

The Accurate indoor localization is a challenging task due to the absence of GPS. Among numerous proposals, Wifi fingerprint-based localization is one of the most promising approach, since most buildings are nowadays equipped with Wifi access points for wireless network coverage. Due to the nature of Wifi access points in which any user can deploy and manage their own, fingerprints from some access points lead to estimation errors. Location estimation algorithms should consider these factors and be able to locate users with low error distance. Finding the nearest neighbor using Euclidean distance in signal space is most widely used method in estimating location. However, this paper shows that Euclidean distance is prone to error when unstable access points are present. Also, Euclidean distance does not differentiate strong signals and weak signals, which can also mislead location estimation. We propose a different way to determine the nearest neighbor, which penalizes signals from unstable access points, and signifies strong signals compared to weak signals. Experiments with real measurements show that the proposed algorithm reduces mean error distance by 57% and 90-percentile error distance by 64% compared to the Euclidean distance method.

무선 센서 네트워크에서, 각 센서 노드들로부터 수집된 정보를 효율적으로 활용하기 위해 센서 노드의 정확한 위치 정보는 필수적이다. 센서 노드의 위치를 추정하는 다양한 기법들 중, 일반적으로 많이 사용되는 수신신호세기(RSS)기법은 추가적인 하드웨어 자원 없이 쉽게 구현될 수 있으나 채널 환경에 따라 다양한 표본 데이터들이 수집 될 수 있고, 특히 이상점(outlier)이 포함 될 수 있다. 이러한 이상점들은, 수집된 표본들로부터 통계적 분석(statistical analysis)에 상당한 요인을 미치며 위치 추정 오차를 발생시키는 주요한 원인이 된다. 따라서 본 논문에서는, 이상점이 포함 된 표본들로부터 정확한 위치 추정을 위해 Robust Statistics를 적용한 가우시안 필터 알고리즘을 제안한다. 제안한 알고리즘은 이상점이 포함된 표본들로부터 이상점을 제거하고, 낮은 확률값의 표본들을 배제함으로써 위치 추정의 정확도를 향상시킨다. 시뮬레이션 결과로부터, 이상점이 포함 된 표본들로부터 비 가우시안적 환경에서 제안된 방법의 위치 추정의 정확성 향상과 강인성을 확인하였다.

In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.