Earticle

본 연구의 목적은 장애인 스키 강습의 문제점과 장애인 스키 강습 중요 요인 탐색을 통한 장애인 스키 지도 매뉴얼 을 개발 및 검증하는 것이다. 연구목적 달성을 위한 조사대상은 2015년 대한장애인체육회 스키 학교 지도자 14명, 특수체육 전공교수 3명, 장애인 스키 전문가 3명, 장애인 스키 국가대표 지도자 3명 등 총23명을 대상으로 선정하였다. 2015 대한장애 인체육회 스키학교 지도자를 대상으로 장애인 스키 활성화 장애요인에 대한 개방형 설문조사를 통해 얻은 결과는 지도자, 지도 프로그램, 정책, 시설, 장비, 홍보부족, 접근성, 무관심, 대상 부족 등의 요인이 도출되었으며, 이 요인들 중 정책, 시설, 프로그램, 지도자, 장비 요인을 대상으로 우선순위를 분석한 결과 프로그램이 가장 높은 가중치를 갖는 것으로 나타났으며, 지도자, 장비, 시설, 정책 순으로 우선순위가 나타났다. 마지막으로 장애인 스키 지도 매뉴얼 개발 결과는 장애유형에 따라 투-트랙, 쓰리-트랙, 포-트랙, 모노스키 네 가지 유형으로 분류되었다.

The purpose of the research is to figure out the problem of skiing-lesson to handicapped person and to develop and verify skiing-lesson manual by exploring skiing lesson’s main factors of handicapped. To achieve the object of the research, we have done a survey to 14 people who agreed on participating in the research by doing open type-questionnaire survey to explore the obstructive factors of ski-vitalization for the handicapped. We have chosen two professor of adapted physical education, two ski specialists for the handicapped, and two instructors for national ski team to the adaptive to analyze the priority of ski-lesson to handicapped. There were a few results the result of developing the skiing-lesson manual for the handicapped was classified by 4types according to the types of disability. (two-track, three-track, four-track, monoski).

독립 협동형 감시시스템인 이차감시레이더(SSR)의 위치 정보의 해상도를 높이기 위해 다변측정감시시스템(MLAT)이 항공 감시시스템에 적용되고 있다. 다변측정감시시스템은 쌍곡선(hyperbola) 또는 쌍곡면(hyperboloid) 위치 측정법을 이용하는 시스템으로 4개 이상의 지상 수신기에서 TOA (time of arrival)값을 측정하고 이를 중앙처리장치에서 각 수신기 상호간의 수신시각차 (TDOA; time difference of arrival)를 이용하여 항공기의 위치를 계산한다. TDOA (time difference of arrival) 알고리즘을 이용하는 다변측정감시시스템의 위치 측정 해상도를 높이기 위해서는 먼저 항공기에 탑재된 트랜스폰더에서 송출된 신호가 수신기에 수신되는 시간인 TOA(time of arrival)를 정밀하게 측정하여야 한다. 본 논문에서는 다변측정감시시스템의 구성에 대해 간략히 설명하였으며 TOA를 측정하기 위한 방법 중 하나인 ATD (adaptive threshold detector)에 대하여 간략히 설명하였다. ATD는 동일 신호에 대한 수신기간 수신한 신호의 진폭 차에 의해 발생하는 펄스 위치 편차 문제를 해결하는 방식이다. 본 논문에서는 ATD의 종류 중 LAS (level adjuster System)과 CDS (constant-fraction discriminator system)에 대한 시뮬레이션 결과와 기본적인 임계값을 적용한 펄스 위치 계산 시뮬레이션 결과를 비교하여 ATD의 성능을 분석하였다.

MLAT which is an independent cooperative surveillance system is applied to increase the positon resoultin of secondary survelliance radar. MLAT uses the hyperboic or hyperboloid position mesurement algorithm. Central processing unit of MLAT calculates target position using time difference of arrival (TDOA) which can be solved from time of arrival (TOA) information of each receivers (at least 4 receivers). To increase position resolution of MLAT which use TDOA, TOA which is transfer time from tranmitter to receiver shold be calculated with precision time resolution in receiver. This paper explained the MLAT system briefly and explained ATD which is one of means of calcuating pulse position. ATD is applied to solve the deviation of pulse position due to different amplitude of signals in mulitiple receivers. In this paper, to analysis the performance of ATD, the simulation result of LAS and CDS was compared with the simulation result of basic threshold method.

In case of environment requiring safety such as human body and requiring flexible shape, a conventional mechanical actuator system does not satisfy requirements. Therefore, in order to solve these problems. a research of various smart material such as EAP (Electro Active Polymer), EAC (Electro Active Ceramic) and SMA (Shape Memory Alloy) is in progress. Recently, the highest preferring material among various smart material is EP (Electrostictive Polymer), because it has very fast response time, powerful force and large displacement. The previous researches have been studied properties of polymer and simple control, but present researches are studied a polymer actuator. An EP (Electostrictive Polymer) actuator has properties which change variably ils shape and environmental condition. Therefore, in order to coincide with a user's purpose, it is important not only to decide a shape of actuator and mechanical design but also to investigate a efficient controller. In this paper, we constructed the control logic with an adaptive fuzzy algorithm which depends on the physical properties of EP that has a dielectric constant depending on time. It caused for a sub-actuator to operate at the same time that a sub-actuator system operation increase with a functional improvement and control efficiency improvement in each actuator, hence it becomes very important to manage it effectively and to control the sub-system which Is operated effectively. There is a limitation on the management of Main-host system which has multiple sub-system, hence it brings out the Multi-Vehicle Control process that disperse the task efficiently. Controlling the multi-dispersion system efficiently, it needs the research of Main-host system's scheduling, data interchange between sub-actuators, data interchange between Main-host system and sub-actuator system, and data communication process. Therefore in this papers, we compared the fuzzy controller with the adaptive fuzzy controller. also, we applied the scheduling method for efficient multi-control in EP Actuator and the algorithm with interchanging data, protocol design.

In this paper, the development of a new group controller for high-speed elevator is carried out utilizing the approach of an adaptive dual fuzzy logic. Some goals of control are the minimization of waiting time, mean-waiting time and long-waiting time in a high building, when a new hall call is generated, adaptive dual fuzzy controller evaluate traffic pattern and change appropriately the membership function of a fuzzy rule base. Controls for co-operation among elevators in a group control algorithm arte essential, and the most critical control function in the group controller is an effective and proper hall call assignment of elevators. The group elevator system utilizing adaptive dual fuzzy control reveals a great deal of improvement on its performance.

This paper is concerned with the position control of electrohydraulic servo system under parameter variation. An adaptive sliding mode control which uses the direct parameter estimation scheme, is proposed to design a robust controller for fast and accurate control of the system. It is shown that the adaptive sliding mode control algorithm is robust and effective in attaining fast and accurate position control of system under time-dependent parameter variation. It is also shown experimentally that chattering phenomena in a sliding mode control can significantly be reduced by using boundary layer technique, and that new approach in sliding mode control introducing a term proportional to the distance between the current state and the sliding surface in the control law is effective to obtain fast response and to increase stability of the system. Computer simulation on the dynamic performance of the control system is also presented.

최근 다양한 형태의 데이터를 임베딩하여 고차원 벡터로 표현하고, 이를 기반으로 의미적으로 연관된 데이터를 탐색하려는 연구가 활발히 진행되고 있다. 이에 따라 벡터 유사도 검색뿐 아니라, 구조적 조건이 결합된 filtered vector search, 그리고 의미적으로 연결 가능한 레코드 쌍을 찾는 vector similarity join 및 semantic join 연구에 대한 관심이 증가하고 있다. 본 연구에서는 테이블 레코드 단위의 임베딩 벡터를 대상으로 의미 기반 조인을 수행하는 Adaptive-K Join 알고리즘을 제안한다. 기존의 Nested Loop Join(NLJ)은 모든 레코드 쌍을 비교하므로 정확한 결과를 보장하지만 계산 비용이 매우 크고, Hierarchical Navigable Small World(HNSW)는 빠른 ANN 탐색을 제공하지만, 그래프 기반의 근사 탐색 구조와 고정된 후보 수 k의 제약으로 인해 threshold 기반 similarity join에서 필요한 모든 후보를 포함하지 못할 수 있다. 특히 실제 환경과 같이 질의별 매칭 수 분포가 불균형한 데이터셋에서는 이러한 한계가 더욱 두드러진다. 제안 방법은 작은 초기 k에서 탐색을 시작한 뒤, 현재 top-k 후보의 경계 유사도를 기준으로 필요한 경우에만 k를 점진적으로 확장함으로써 불필요한 후보 검증을 줄이면서 더 많은 조인 가능한 레코드 쌍을 탐색하도록 설계되었다. 실험 결과, 임계값 0.6 기준에서 제안 방법은 NLJ의 8342.4초에 비해 42.2초의 실행 시간을 보이면서도, 고정된 k를 사용하는 HNSW의 77.2%보다 높은 95.9% recall을 달성하였다. 결과적으로 제안 방법은 HNSW보다 더 높은 recall을 유지하면서도 NLJ보다 빠른 실행 시간을 달성하였다. 또한 제안 방법은 명시적인 키가 없는 환경에서도 의미적으로 유사한 레코드를 연결할 수 있어, 데이터 소스 간 정보 보강과 의미 기반 데이터 통합에 활용될 수 있을 것으로 보인다.

Recent studies have explored representing diverse data as high-dimensional embedding vectors and using them to search for semantically related data. Beyond vector similarity search, growing attention has been paid to filtered vector search with structural conditions and to vector similarity join and semantic join for finding semantically related record pairs. In this study, we propose Adaptive-K Join, an algorithm that performs semantic joins on table record embeddings. Nested Loop Join (NLJ) guarantees exact results by comparing all record pairs, but is computationally expensive. Hierarchical Navigable Small World(HNSW) enables fast ANN search, but its approximate graph-based search and fixed candidate size k may fail to retrieve all matches required for threshold-based similarity join. This limitation is more pronounced in real-world datasets with imbalanced match distributions across queries. Adaptive-K Join starts with a small initial k and expands it only when needed based on the boundary similarity of the current top-k candidates. This reduces unnecessary candidate verification while exploring more matching record pairs. At a threshold of 0.6, the proposed method achieved 42.2 seconds runtime, compared with 8342.4 seconds for NLJ, and 95.9% recall, outperforming fixed-k HNSW at 77.2%. These results show that Adaptive-K Join achieves higher recall than HNSW while remaining much faster than NLJ. It can also connect semantically similar records without explicit keys, making it applicable to information enrichment across data sources and semantic-based data integration.

This paper presents a preliminary study on the development of a Light-Emitting Diode (LED) fishing lamp system that can maintain a stationary irradiation region despite ship rolling motions. The proposed system is composed of three modules which include a main control module, a fishing lamp module, and a sensor module. The main control module collects both the operating condition setting of the fishing lamp via the input panel and the tilt angle information of a fishing boat from the sensor module. It provides the conditions and information to the fishing lamp module for controlling the LED modules that maintain a stationary irradiation region. One fishing lamp module is composed of 22 LED modules, a lamp controller, and a temperature sensor. The sensor module is used for measuring the tilt angle of the fishing boat. Tests were carried out to verify the reliability of the LED module driver and the sensor module. The tests showed that the LED modules were reliably controlled, in accordance with the rolling motion of a fishing boat.

연합 학습(FL)은 여러 공동 작업자 간에 분산된 모델 학습을 위한 강력한 방법론으로 부상해 데이터 공유의 필요성을 없애준다. FL은 데이터 프라이버시를 보호하는 기능으로 호평을 받고 있지만, 다양한 유형의 프라이버시 공격으로부터 자유롭지 않다. 대표적인 개인정보 보호 기술인 차분 프라이버시(DP)는 이러한 취약점에 대응하기 위해 널리 사용된다. 이 논문에서는 기존의 작업별 적응형 DP 메커니즘을 FL 환경에 적용해 성능을 평가한다. 포괄적인 분석을 통해 다양한 DP 메커니즘이 공유 글로벌 모델의 성능에 미치는 영향을 평가하며, 특히 다양한 데이터 배포 및 분할 스키마에 주의를 기울인다. 이를 통해, FL에서 개인정보 보호와 유용성 간의 복잡한 상호 작용에 대한 이해를 심화하고, 성능 저하 없이 데이터를 보호할 수 있는 검증된 방법론을 제공한다.

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

높은 신뢰도의 댐 유입량 예측은 효율적인 댐 운영을 위해 필요하다. 최근 다층퍼셉트론(Multi Layer Perceptron, MLP)을 활용하여 댐의 유입량을 예측하는 연구들이 진행되었다. 기존 연구들은 MLP의 연산자 중 자료 간의 최적 상관관계를 찾는 optimizer로 경사하강법(Gradient Descent, GD) 기반의 optimizer를 사용하였다. 하지만, GD 기반의 optimizer들은 지역 최적값으로의 수렴 가능성과 저장공간 부재로 인해 예측성능이 저하된다는 단점이 있다. 본 연구는 GD 기반 optimizer 중 Adaptive moments와 Improved Harmony Search (IHS)를 결합한 Adaptive moments combined with Improved Harmony Search (AdamIHS)를 개발하여 GD 기반 optimizer의 단점을 개선하였다. AdamIHS를 사용한 MLP의 학습 및 예측성능을 평가하기 위해 대청댐 유입량을 학습 및 예측하였으며, GD 기반 optimizer를 사용한 MLP의 학습 및 예측성능과 비교하였다. 학습결과를 비교하면, AdamIHS를 사용한 은닉층 5개인 MLP의 Mean Squared Error (MSE) 평균값이 11,577로 가장 낮았다. 예측결과를 비교하면, AdamIHS를 사용한 은닉층 1개인 MLP의 MSE 평균값이 413,262로 가장 낮았다. 본 연구에서 개발된 AdamIHS를 활용하면 다양한 분야에서 향상된 예측성능을 보여줄 수 있을 것이다.

High-reliability prediction of dam inflow is necessary for efficient dam operation. Recently, studies were conducted to predict the inflow of dams using Multi Layer Perceptron (MLP). Existing studies used the Gradient Descent (GD)-based optimizer as the optimizer among MLP operators to find the optimal correlation between data. However, the GD-based optimizers have disadvantages in that the prediction performance is deteriorated due to the possibility of convergence to the local optimal value and the absence of storage space. This study improved the shortcomings of the GD-based optimizer by developing Adaptive moments combined with Improved Harmony Search (AdamIHS), which combines Adaptive moments among GD-based optimizers and Improved Harmony Search (IHS). In order to evaluate the learning and prediction performance of MLP using AdamIHS, Daecheong Dam inflow was learned and predicted and compared with the learning and prediction performance of MLP using GD-based optimizer. Comparing the learning results, the Mean Squared Error (MSE) of MLP, which is 5 hidden layers using AdamIHS, was the lowest at 11,577. Comparing the prediction results, the average MSE of MLP, which is one hidden layer using AdamIHS, was the lowest at 413,262. Using AdamIHS developed in this study, it will be possible to show improved prediction performance in various fields.

무선 스마트카드는 빌딩 보안 어플리케이션에 주된 역할을 하고 있다. 본 연구는 사용자에게 보다 편리함을 주고 스마트카드 시스템의 성능 개선을 제안한다. 스마트카드는 카드 리더기에 삽입을 하지 않고 장치에서 수센티미터 떨어져도 접근이 허용된다. 적응형 변조와 오류 제어를 통해 전력소비를 제어하는 성능은 필요하다. 본 논문에서는 적응형 리드 솔로몬 코드 기반의 FEC와 링크에서 M계수를 변화시킨 M-FSK변조에 대하여 연구한다. 적응형 변조와 적응형 오류 정정은 다른 다양한 정적스키마와 에너지 효율을 비교한 결과 50% 이상 에너지를 저장한다.

Since the wireless smart card has played a main role in the identification security application for the building access; this research has its purpose to improve the performance of the smart card system and aims to offer more convenient to user. The contactless cards do not require insertion into a card reader and can work up to centimeters away from the reading device. To be able to cope with this performance the controlling of power consumption through the adaptive modulation and error control is needed. This paper addresses a forward error control (FEC) scheme with the adaptive Reed-Solomon code rate and an M-ary frequency shift keying (M-FSK) modulation scheme with the varying symbol size M over the link. The result of comparing energy efficiencies of adaptive error correction and adaptive modulation to other various static schemes shows to save over 50% of the energy consumption.

전 세계적으로 빌딩 에너지 세이빙에 대한 관심이 증가하고 있으며, BEMS(Building Energy Management System)을 효율적으로 운용하기 위한 IT 기술에 대한 연구를 지속적으로 진행하고 있다. 최근 LED 조명기술의 발전으로 LED를 제어하여 에너지 절감효과 를 극대화 할 수 있으며 BEMS에 이러한 LED 조명 제어기술들이 개발되고 있다. 본 논문에서는 건물에 설치되어 있는 IP 카메라와 Adaptive GMM(Gaussian Mixture Model)을 이용하여 BEMS용 LED 조명제어에 대한 시스템을 제안하였다. 설계한 구조를 기반으 로 빌딩의 영상을 실시간으로 모니터링하고, 동적 객체를 영상추적하며, 다수의 객체를 클러스터링하고 인체 이동을 감지하여 LED 조명을 제어하는 기능을 제안하고, 관련 Software 개발을 통해 구현가능성을 입증하였다.

There has been increased interest for building energy-saving for worldwide. There is continuing research on IT technology for efficient management of BEMS. Recently, It be able to control of LED and to maximize energy savings to the development of LED lighting technology. We propose the security image processing system to improve efficiency and we implement the real-time status monitoring system to surveil the object in the building energy management system. In this paper, we proposed the system of LED control using IP camera and Adaptive Gaussian Mixture Model for BEMS . We implement LED light control software on the based of the security camera image processing so the reliable controling based on the security camera is possible efficiently.

Overlay parameter control of the semiconductor photolithography process is researched in this paper. Overlay parameters denote the error in superposing the current pattern to the pattern previously created. The reduction of the overlay deviation is one of the key factors in improving the quality of the semiconductor products. The semiconductor process is affected by numerous environment and equipment factors. Through process condition prediction and control, the overlay inaccuracy can be reduced. Generally, three types of process condition change exist; uncontrollable white noise, slowly changing drift, and abrupt condition shift. To effectively control the aforementioned process changes, control scheme using adaptive deadband is proposed. The suggested approach and existing control method are cross evaluated through simulation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder defined by amyloid-β (Aβ) plaques, tau hyperphosphorylation, and neuroinflammation. Although earlier work emphasized brain-resident glia (microglia and astrocytes), recent studies highlight adaptive immune cells, particularly T and B lymphocytes, as modulators of AD pathology. This review synthesizes animal and human findings from 2022-2025 to provide updated insights into the multifaceted roles and therapeutic potential of adaptive immunity in AD. Infiltration of peripheral T and B cells into the brain parenchyma links peripheral immunity to central nervous system (CNS) pathology. Both infiltrating lymphocytes and resident glia show context-dependent dual effects, either exacerbating neurodegeneration or promoting neuroprotection. Therapeutic strategies under active investigation include modulation of CD4+ T cell differentiation, adoptive transfer of regulatory T cells, and next-generation active vaccines for AD. Overall, selective modulation of discrete immune subsets may enable adaptive-immunity-based treatments, a complex yet promising avenue for AD therapy.

Objective: To evaluate whether a 12-week adaptive exergame-based balance training program reduces total sway velocity during quiet standing more than dose-matched conventional exercise training in adolescents with developmental coordination disorder (DCD) or autism spectrum disorder (ASD). Design: Assessor-blinded, two-arm, parallel-group randomized controlled trial protocol. Methods: Sixty-eight adolescents aged 12-18 years with clinically diagnosed DCD or ASD and confirmed balance impairments will be recruited. Participants will be randomized 1:1 to an experimental group receiving adaptive exergame-based balance training (AIRPASS XR system) or a control group receiving conventional exercise training. Both groups will complete 40-minute sessions three times weekly for 12 weeks, with matched intervention duration. The experimental group will use a real-time algorithm that adjusts task difficulty according to success rates and center-of-pressure parameters. The primary outcome is the change in total sway velocity, a sensitive measure of postural control efficiency associated with fall risk, assessed using a force plate during a 30-second quiet standing task from baseline (T0) to post-intervention (T1). Secondary outcomes include spatiotemporal gait parameters, motor coordination, balance under sensory-challenge conditions, and treatment adherence. Results: The study will evaluate whether the adaptive exergame group achieves greater improvements than the conventional exercise group. Conclusions: This protocol addresses key methodological limitations by incorporating performance-based difficulty adjustment and objective movement analysis. If effective, the findings will support individualized digital rehabilitation strategies for adolescents with neurodevelopmental disabilities.

Although federated learning (FL) has emerged as a revolutionary paradigm for privacy-preserving distributed learning, the non-independent and identically distributed nature of client data poses significant challenges to model performance. This study proposes a personalized FL framework that integrates dynamic client sampling and advanced personalization techniques. The proposed method effectively addresses data heterogeneity, enhances client-specific performance, and ensures efficient communication. Experimental results on CIFAR-100 demonstrate significant improvements, with personalized models reaching accuracies between 53% and 57% compared to 23.13% for the global model, while maintaining stable communication costs of 376,588 bytes per round. Furthermore, our framework demonstrates robust performance across different levels of data heterogeneity, maintaining consistent accuracy even when the Gini coefficient of the label distribution varies from 0.2 to 0.8. Statistical analysis confirms the significance of these improvements, demonstrating the effectiveness of FedPer in handling heterogeneous data distributions while maintaining computational efficiency, making the proposed method particularly suitable for resource-constrained, privacy-sensitive applications.

Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.