• 동의생리병리학회지
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• 제35권1호
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• pp.22-27
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• 2021

This study conducted a questionnaire for students of Pusan National University Graduate School of Korean Medicine who practiced using the Oriental Medicine Diagnosis System (ODS). From the questionnaire, this study investigated current state of application and perception of AI in Korean Medicine and explored the direction of ODS improvement and utilization. The survey questions consisted of six questions examining the satisfaction of the diagnostic expert system, five questions evaluating the availability of the diagnostic expert system, and six questions to predict the impact of AI on the Korean medicine community. The survey analysis showed high satisfaction with practice using ODS. On the other hand, the possibility of using ODS, especially in clinical use, was evaluated as relatively low compared to the satisfaction of the practice. Therefore, the overall impact of AI on the Korean medical community is not expected to be large. Although there are difficulties in standardization of clinical data due to the academic characteristics of Korean medicine, it is necessary to continue attempts to apply AI. By actively introducing educational tools using the latest AI techniques to the diagnosis experience and doctor-patient role in a practice, students will be able to increase their satisfaction with their practice and respond appropriately to the state-of-the-art medical environment.

• Parasites, Hosts and Diseases
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• 제60권1호
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• pp.25-34
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• 2022

Alveolar echinococcosis (AE) caused by infection with E. multilocularis metacestode, represents one of the most fatal helminthic diseases. AE is principally manifested with infiltrative, proliferating hepatic mass, resembling primary hepatocellular carcinoma. Sometimes metastatic lesions are found in nearby or remote tissue. AE diagnosis largely depends on imaging studies, but atypical findings of imaging features frequently require differential diagnosis from other hepatic lesions. Serological tests may provide further evidence, while obtaining reliable AE materials is not easy. In this study, alternative antigens, specific to AE were identified by analyzing E. granulosus protoscolex proteins. An immunoblot analysis of E. granulosus protoscolex showed that a group of low-molecular-weight proteins in the range from 14 kDa to 16 kDa exhibited a sensitive and specific immune response to AE patient sera. Partial purification and proteomic analysis indicated that this protein group contained myosin, tubulin polymerization promoting protein, fatty-acid binding protein, uncharacterized DM9, heat shock protein 90 cochaperone tebp P-23, and antigen S. When the serological applicability of recombinant forms of these proteins was assessed using enzyme-linked immunosorbent assay, DM9 protein (rEgDM9) showed 90.1% sensitivity (73/81 sera tested) and 94.5% specificity (172/181 sera tested), respectively. rEgDM9 showed weak cross-reactions with patient sera from the transitional and chronic stages of cystic echinococcosis (3 to 5 stages). rEgDM9 would serve as a useful alternative antigen for serodiagnosis of both early- and advanced-stage AE cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1216-1233
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• 2023

Conventional occupational therapy (OT) is conducted under the observation of an occupational therapist, and there are limitations in measuring and analyzing details such as degree of hand tremor and movement tendency, so this important information may be lost. It is therefore difficult to identify quantitative performance indicators, and the presence of observers during performance sometimes makes the subjects feel that they have to achieve good results. In this study, by using the Unity3D and artificial intelligence (AI) speaker, we propose a system that allows the subjects to steadily use it by themselves and helps the occupational therapist objectively evaluate through quantitative data. This system is based on the OT of the sensory integration approach. And the purpose of this system is to improve children's activities of daily living by providing various feedback to induce sensory integration, which allows them to develop the ability to effectively use their bodies. A dynamic OT cognitive assessment tool for children used in clinical practice was implemented in Unity3D to create an OT environment of virtual space. The Leap Motion Controller allows users to track and record hand motion data in real time. Occupational therapists can control the user's performance environment remotely by connecting Unity3D and AI speaker. The experiment with the conventional OT tool and the system we proposed was conducted. As a result, it was found that when the system was performed without an observer, users can perform spontaneously and several times feeling ease and active mind.

• 한국IT서비스학회지
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• 제14권1호
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• pp.99-113
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.66-78
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• 2009

A mobile ultrasound scanner developed for use in point-of-care applications is introduced, which can not only provide ultrasound images but can also measure various bio-signals. The mobile ultrasound scanner is also designed to meet the demanding requirements for point-of-care diagnosis, such as battery-powered operation, portability in terms of size and weigh, and real-time wireless communications capability for remote diagnosis. To meet these requirements, an efficient beamforming method for high resolution imaging with a small number of active elements, a hardware efficient beamformer architecture, and echo processing algorithms with greatly reduced computational complexity have been developed. Experimental results show that the prototype mobile ultrasound scanner is fully functional and satisfies most of the design requirements.

• 전기학회논문지
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• 제67권7호
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• pp.910-916
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• 2018

In this paper, we developed a shoulder rehabilitation exercise device and monitoring system to remotely provide rehabilitation system for the ones who need shoulder exercises including the patients with rotator cuff rupture. In order to evaluate the severity of shoulder muscle injury, a total of 4 shoulder rehabilitation exercises ((3) shoulder abduction, (2) shoulder flexion, (3) shoulder abduction with elbow flexion, (4) shoulder extension with elbow flexion) were selected and instructed to be performed with a 3 kg dumbbell for 5 times. For EMG (electromyogram) signal analysis, each subject's maximum voluntary contraction (MVC) was measured. EMG signals reflect the activation level of contracting muscles during dynamic exercises. Six participants' muscle activation levels in posterior deltoid, middle deltoid, upper trapezius, and infraspinatus were measured and compared. The mean power spectrum values in the time and frequency domains were compared between two age-matched groups (20s and 50s). The results showed lower muscle activation in the elderly subjects (n=3) compared to that of the ones in their twenties (n=3).

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.667-670
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• 2007

착용형 컴퓨팅을 응용한 건강모니터링 분야는 급격한 성장이 이루어지고 있다. 본 연구의 목적은 유비쿼터스 헬스케어를 위해 착용형 ECG모니터링 시스템을 구현하는 것이다. 본 논문에서는 구현된 프로토타입의 착용형 건강모니터링 시스템의 계측성능, 무선전송, 계측된 ECG신호의 분석 등에 대해 기술하고자 한다. 구현된 하드웨어 시스템은 지그비 통신을 이용하여 무선으로 체스트 벨트타입의 센서에서 PC서버로 전송한다. 그리고 구현된 시스템을 이용하여 ECG 모니터링 테스트를 수행한 결과 원격 모니터링의 가능성을 확인하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.