• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.295-305
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• 2024

This study aims to analyze the micro-sedimentary structures of the Hwangdo tidal flats using ultra-high resolution unmanned aerial vehicle (UAV) data. Tidal flats, located in the transitional area between land and sea, constantly change due to tidal activities and provide a unique environment important for understanding sedimentary processes and environmental conditions. Traditional field observation methods are limited in spatial and temporal coverage, and existing satellite imagery does not provide sufficient resolution to study micro-sedimentary structures. To overcome these limitations, high-resolution images of the Hwangdo tidal flats in Chungcheongnam-do were acquired using UAVs. This area has experienced significant changes in its sedimentary environment due to coastal development projects such as sea wall construction. From May 17 to 18, 2022, sediment samples were collected from 91 points during field surveys and 25 in-situ points were intensively analyzed. UAV data with a spatial resolution of approximately 0.9 mm allowed identifying and extracting parameters related to micro-sedimentary structures. For mud cracks, the length of the major axis of the polygons was extracted, and the wavelength and ripple symmetry index were extracted for ripple marks. The results of the study showed that in areas with mud content above 80%, mud cracks formed at an average major axis length of 37.3 cm. In regions with sand content above 60%, ripples with an average wavelength of 8 cm and a ripple symmetry index of 2.0 were formed. This study demonstrated that micro-sedimentary structures of tidal flats can be effectively analyzed using ultra-high resolution UAV data without field surveys. This highlights the potential of UAV technology as an important tool in environmental monitoring and coastal management and shows its usefulness in the study of sedimentary structures. In addition, the results of this study are expected to serve as baseline data for more accurate sedimentary facies classification.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Radiation Oncology Journal
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• v.21 no.1
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• pp.54-65
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• 2003

Purpose : To analyze the gene expression Profiles of uterine ceulcal cancer, and its variation after radiation therapy, with or without concurrent chemotherapy, using a CDNA microarray. Materials and Methods :Sixteen patients, 8 with squamous ceil carcinomas of the uterine cervix, who were treated with radiation alone, and the other 8 treated w14h concurrent chemo-radiation, were Included in the study. Before the starling of the treatment, tumor biopsies were carried out, and the second time biopsies were peformed after a radiation dose of 16.2$\~$27 Gy. Three normal cervix tissues were used as a control group. The microarray experiments were peformed with 5 groups of the total RNAs extracted individually and then admixed as control, pre-radiation therapy alone, during-radiation therapy alone, pre-chemoradiation therapy, and during-chemoradlation therapy. The 33P-iabeled CDNAS were synthesized from the total RNAs of each group, by reverse transcription, and then they were hybridized to the CDNA microarray membrane. The gene expression of each microarrays was captured by the intensity of each spot produced by the radioactive isotopes. The pixels per spot were counted with an Arrayguage, and were exported to Microsoft Excel The data were normalized by the Z transformation, and the comparisons were peformed on the Z-ratio values calculated. Results : The expressions of 15 genes, including integrin linked kinase (ILK), CDC28 protein kinase 2, Spry 2, and ERK 3, were increased with the Z-ratio values of over 2.0 for the cervix cancer tissues compared to those for the normal controls. Those genes were involved In cell growth and proliferation, cell cycle control, or signal transduction. The expressions of the other 6 genes, Including G protein coupled receptor kinase 5, were decreased with the Z-ratio values of below -2.0. After the radiation thorapy, most of the genes, with a previously Increase expressions, represented the decreased expression profiles, and the genes, with the Z-ratio values of over 2.0, were cyclic nucleotlde gated channel and 3 Expressed sequence tags (EST). In the concurrent chemo-radiation group, the genes involved in cell growth and proliferation, cell cycle control, and signal transduction were shown to have increased expressions compared to the radiation therapy alone group. The expressions of genes involved in anglogenesis (angiopoietln-2), immune reactions (formyl peptide receptor-iike 1), and DNA repair (CAMP phosphodiesterase) were increased, however, the expression of gene involved In apoptosls (death associated protein kinase) was decreased. Conclusion : The different kinds of genes involved in the development and progression of cervical cancer were identified with the CDNA microarray, and the proposed theory is that the proliferation signal stalls with ILK, and is amplified with Spry 2 and MAPK signaling, and the cellular mitoses are Increased with the increased expression oi Cdc 2 and cell division kinases. After the radiation therapy, the expression profiles demonstrated 4he evidence of the decreased cancer cell proliferation. There was no sigificant difference in the morphological findings of cell death between the radiation therapy aione and the chemo-radiation groups In the second time biopsy specimen, however, the gene expression profiles were markedly different, and the mechanism at the molecular level needs further study.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.49-78
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• 2005

Ⅰ. 연구(硏究) 배경(背景) 및 목적(目的) o 우리나라 보험시장의 세대가입율은 86%로 보험시장 성숙기에 진입하였으며 기존의 전통적인 전업채널에서 방카슈랑스의 도입, 온라인전문보험사의 출현, TM 영업의 성장세 等멀티채널로 진행되고 있음 o LTC(장기간병), CI(치명적질환), 실손의료보험 등(等)선 진형 건강상품의 잇따른 출시로 보험리스크 관리측면에서 언더라이팅의 대비가 절실한 시점임 o 상품과 마케팅 等언더라이팅 측면에서 매우 밀접한 영역의 변화에 발맞추어 언더라이팅의 인수기법의 선진화가 시급히 요구되는 상황하에서 위험을 적절히 분류하고 평가하는 선진적 언더라이팅 기법 구축이 필수 적임 o 궁극적으로 고객의 다양한 보장니드 충족과 상품, 마케팅, 언더라이팅의 경쟁력 강화를 통한 보험사의 종합이익 극대화에 기여할 수 있는 방안을 모색하고자 함 Ⅱ. 선진보험시장(先進保險市場)Risk 세분화사례(細分化事例) 1. 환경적위험(環境的危險)에 따른 보험료(保險料) 차등(差等) (1) 위험직업 보험료 할증 o 미국, 유럽등(等) 대부분의 선진시장에서는 가입당시 피보험자의 직업위험도에 따라 보험료를 차등 적용중(中)임 o 가입하는 보장급부에 따라 직업 분류방법 및 할증방식도 상이하며 일반사망과 재해사망,납입면제, DI에 대해서 별도의 방법을 사용함 o 할증적용은 표준위험율의 일정배수를 적용하여 할증 보험료를 산출하거나, 가입금액당 일정한 추가보험료를 적용하고 있음 - 광부의 경우 재해사망 가입시 표준위험율의 300% 적용하며, 일반사망 가입시 $1,000당 $2.95 할증보험료 부가 (2) 위험취미 보험료 할증 o 취미와 관련 사고의 지속적 다발로 취미활동도 위험요소로 인식되어 보험료를 차등 적용중(中)임 o 할증보험료는 보험가입금액당 일정비율로 부가(가입 금액과 무관)하며, 신종레포츠 등(等)일부 위험취미는 통계의 부족으로 언더라이터가 할증율 결정하여 적용함 - 패러글라이딩 년(年)$26{\sim}50$회(回) 취미생활의 경우 가입금액 $1,000당 재해사망 $2, DI보험 8$ 할증보험료 부가 o 보험료 할증과는 별도로 위험취미에 대한 부담보를 적용함. 위험취미 활동으로 인한 보험사고 발생시 사망을 포함한 모든 급부에 대한 보장을 부(不)담보로 인수함. (3) 위험지역 거주/ 여행 보험료 할증 o 피보험자가 거주하고 있는 특정국가의 임시 혹은 영구적 거주시 기후위험, 거주지역의 위생과 의료수준, 여행위험, 전쟁과 폭동위험 등(等)을 고려하여 평가 o 일반사망, 재해사망 등(等)보장급부별로 할증보험료 부가 또는 거절 o 할증보험료는 보험全기간에 대해 동일하게 적용 - 러시아의 경우 가입금액 $1,000당 일반사망은 2$의 할증보험료 부가, 재해사망은 거절 (4) 기타 위험도에 대한 보험료 차등 o 비행관련 위험은 세가지로 분류(항공운송기, 개인비행, 군사비행), 청약서, 추가질문서, 진단서, 비행이력 정보를 바탕으로 할증보험료를 부가함 - 농약살포비행기조종사의 경우 가입금액 $1,000당 일반사망 6$의 할증보험료 부가, 재해사망은 거절 o 미국, 일본등(等)서는 교통사고나 교통위반 관련 기록을 활용하여 무(無)사고운전자에 대해 보험료 할인(우량체 위험요소로 활용) 2. 신체적위험도(身體的危險度)에 따른 보험료차등(保險料差等) (1) 표준미달체 보험료 할증 1) 총위험지수 500(초과위험지수 400)까지 인수 o 300이하는 25점단위, 300점 초과는 50점 단위로 13단계로 구분하여 할증보험료를 적용중(中)임 2) 삭감법과 할증법을 동시 적용 o 보험금 삭감부분만큼 할증보험료가 감소하는 효과가 있어 청약자에게 선택의 기회를 제공할수 있으며 고(高)위험 피보험자에게 유용함 3) 특정암에 대한 기왕력자에 대해 단기(Temporary)할증 적용 o 질병성향에 따라 가입후 $1{\sim}5$년간 할증보험료를 부가하고 보험료 할증 기간이 경과한 후에는 표준체보험료를 부가함 4) 할증보험료 반환옵션(Return of the extra premium)의 적용 o 보험계약이 유지중(中)이며, 일정기간 생존시 할증보험료가 반환됨 (2) 표준미달체 급부증액(Enhanced annuity) o 영국에서는 표준미달체를 대상으로 연금급부를 증가시킨 증액형 연금(Enhanced annuity) 상품을 개발 판매중(中)임 o 흡연, 직업, 병력 등(等)다양한 신체적, 환경적 위험도에 따라 표준체에 비해 증액연금을 차등 지급함 (3) 우량 피보험체 가격 세분화 o 미국시장에서는 $8{\sim}14$개 의적, 비(非)의적 위험요소에 대한 평가기준에 따라 표준체를 최대 8개 Class로 분류하여 할인보험료를 차등 적용 - 기왕력, 혈압, 가족력, 흡연, BMI, 콜레스테롤, 운전, 위험취미, 거주지, 비행력, 음주/마약 등(等) o 할인율은 회사, Class, 가입기준에 따라 상이(최대75%)하며, 가입연령은 최저 $16{\sim}20$세, 최대 $65{\sim}75$세, 최저보험금액은 10만달러(HIV검사가 필요한 최저 금액) o 일본시장에서는 $3{\sim}4$개 위험요소에 따라 $3{\sim}4$개 Class로 분류 우량체 할인중(中)임 o 유럽시장에서는 영국 등(等)일부시장에서만 비(非)흡연할인 또는 우량체할인 적용 Ⅲ. 국내보험시장(國內保險市場) 현황(現況)및 문제점(問題點) 1. 환경적위험도(環境的危險度)에 따른 가입한도제한(加入限度制限) (1) 위험직업 보험가입 제한 o 업계공동의 직업별 표준위험등급에 따라 각 보험사 자체적으로 위험등급별 가입한도를 설정 운영중(中)임. 비(非)위험직과의 형평성, 고(高)위험직업 보장 한계, 수익구조 불안정화 등(等)문제점을 내포하고 있음 - 광부의 경우 위험1급 적용으로 사망 최대 1억(億), 입원 1일(日) 2만원까지 제한 o 금융감독원이 2002년(年)7월(月)위험등급별 위험지수를 참조 위험율로 인가하였으나, 비위험직은 70%, 위험직은 200% 수준으로 산정되어 현실적 적용이 어려움 (2) 위험취미 보험가입 제한 o 해당취미의 직업종사자에 준(準)하여 직업위험등급을 적용하여 가입 한도를 제한하고 있음. 추가질문서를 활용하여 자격증 유무, 동호회 가입등(等)에 대한 세부정보를 입수하지 않음 - 패러글라이딩의 경우 위험2급을 적용, 사망보장 최대 2 억(億)까지 제한 (3) 거주지역/ 해외여행 보험가입 제한 o 각(各)보험사별로 지역적 특성상 사고재해 다발 지역에 대해 보험가입을 제한하고 있음 - 강원, 충청 일부지역 상해보험 가입불가 - 전북, 태백 일부지역 입원급여금 1일(日)2만원이내 o 해외여행을 포함한 해외체류에 대해서는 일정한 가입 요건을 정하여 운영중(中)이며, 가입한도 설정 보험가입을 제한하거나 재해집중보장 상품에 대해 거절함 - 러시아의 경우 단기체류는 위험1급 및 상해보험 가입 불가, 장기 체류는 거절처리함 2. 신체적위험도(身體的危險度)에 따른 인수차별화(引受差別化) (1) 표준미달체 인수방법 o 체증성, 항상성 위험에 대한 초과위험지수를 보험금삭감법으로 전환 사망보험에 적용(최대 5년(年))하여 5년(年)이후 보험 Risk노출 심각 o 보험료 할증은 일부 회사에서 주(主)보험 중심으로 사용중(中)이며, 총위험지수 300(8단계)까지 인수 - 주(主)보험 할증시 특약은 가입 불가하며, 암 기왕력자는 대부분 거절 o 신체부위 39가지, 질병 5가지에 대해 부담보 적용(입원, 수술 등(等)생존급부에 부담보) (2) 비(非)흡연/ 우량체 보험료 할인 o 1999년(年)최초 도입 이래 $3{\sim}4$개의 위험요소로 1개 Class 운영중(中)임 S생보사의 경우 비(非)흡연우량체, 비(非)흡연표준체의 2개 Class 운영 o 보험료 할인율은 회사, 상품에 따라 상이하며 최대 22%(영업보험료기준)임. 흡연여부는 뇨스틱을 활용 코티닌테스트를 실시함 o 우량체 판매는 신계약의 $2{\sim}15%$수준(회사의 정책에 따라 상이) Ⅳ. 언더라이팅 기법(技法) 선진화(先進化) 방안(方案) 1. 직업위험도별 보험료 차등 적용 o 생 손보 직업위험등급 일원화와 연계하여 3개등급으로 위험지수개편, 비위험직 기준으로 보험요율 차별적용 2. 위험취미에 대한 부담보 적용 o 해당취미를 원인으로 보험사고(사망포함) 발생시 부담보 제도 도입 3. 표준미달체 인수기법 선진화를 통한 인수범위 대폭 확대 o 보험료 할증법 적용 확대를 통한 Risk 헷지로 총위험지수 $300{\rightarrow}500$으로 확대(거절건 최소화) 4. 보험료 할증법 보험금 삭감 병행 적용 o 삭감기간을 적용한 보험료 할증방식 개발, 고객에게 선택권 제공 5. 기한부 보험료할증 부가 o 위암, 갑상선암 등(等)특정암의 성향에 따라 위험도가 높은 가입초기에 평준할증보험료를 적용하여 인수 6. 보험료 할증법 부가특약 확대 적용, 부담보 병행 사용 o 정기특약 등(等)사망관련 특약에 할증법 확대, 생존급부 특약은 부담보 7. 표준체 고객 세분화 확대 o 콜레스테롤, HDL 등(等)위험평가요소 확대를 통한 Class 세분화 Ⅴ. 기대효과(期待效果) 1. 고(高)위험직종사자, 위험취미자, 표준미달체에 대한 보험가입 문호개방 2. 보험계약자간 형평성 제고 및 다양한 고객의 보장니드에 부응 3. 상품판매 확대 및 Risk헷지를 통한 수입보험료 증대 및 사차익 개선 4. 본격적인 가격경쟁에 대비한 보험사 체질 개선 5. 회사 이미지 제고 및 진단 거부감 해소, 포트폴리오 약화 방지 Ⅵ. 결론(結論) o 종래의 소극적이고 일률적인 인수기법에서 탈피하여 피보험자를 다양한 측면에서 위험평가하여 적정 보험료 부가와 합리적 가입조건을 제시하는 적절한 위험평가 수단을 도입하고, o 언더라이팅 인수기법의 선진화와 함께 언더라이팅 인력의 전문화, 정보입수 및 시스템 인프라의 구축 등이 병행함으로써, o 보험사의 사차손익 관리측면에서 뿐만 아니라 보험시장 개방 및 급변하는 보험환경에 대비한 한국 생보언더라이팅 경쟁력 강화 및 언더라이터의 글로벌화에도 크게 기여할 것임.