• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.45-55
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• 2024

This study analyzed the efficiency determinants of specialty construction companies by industry using the DEA model and the Tobit model. The analysis targets are 394 specialty construction companies as of 2022. As a result of analysis of efficiency determinants using 12 company characteristics as independent variables, the biggest problem for specialty construction companies was overall efficiency reduction due to rising labor costs. In addition, in a situation where construction companies' loan regulations are severe, the debt ratio was found to have a positive effect on efficiency. Company size had a different impact by industry, and the number of businesses held, credit score, and total capital turnover had an effect only on some industries. This study presents results that are an advance on existing research in that it strategically analyzes factors for improving the efficiency of specialty construction companies. However, it has limitations such as limiting the analysis to only specialty construction companies subject to external audit, insufficient number of companies subject to analysis by industry, and analyzing relative efficiency in the same category for each industry.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.57-76
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• 2017

Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.1
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• pp.13-24
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• 1994

Purse seiner detects a fish school navigating in full speed with the aid of fish finder, sonar, helicopter, etc., and casts a net quickly to enclose the fish school in purse seine net according to the movement of the fish school, wind, and current. At this moment, if the time of casting a net, direction, speed, and turning circle are net suitable, it is unavoidable to lose fish school founded with hard efforts and we only consume our efforts of casting and hauling the net. Therefore, in order to enclose the fish school to enhance the amount of fish for each casting, the author investigated the type of ships equipped with purse seiners and examined maneuvering tests so that we provide some basic information to figure out the ability of steerage correctly. The results obtained are summarized as follows: 1. Block coefficients of pelagic tuna purse seiners with gross tonnage between 500 and 1500 tons are recorded between 0.50 and 0.55 which are greater than those of off shore purse seiners recorded as between 0.44 and 0.54 and less than those of various cargo ships recorded as between 0.56 and 0.84. 2. L/B, L/D, B/D, B/T, and T/D of the class of gross tonnage between 75 and 130 tons are respectively 4.49, 11.00, 2.45, 2.85 and 0.86 as their average and those of the class of between 500 and 1500 tons are 4.89, 10.53, 2.15, 2.73 and 0.75 respectively, which are quite different from those of various cargo ships recorded as 6.0~7.5, 11.0~12.0, 1.6~2.0, 2.2~2.8 and 0.65~0.75 respectively. 3. Rudder area ratio of purse seiners of the class of between 75 and 130 tons is 1/24~1/31 and that of the clase of between 500 and 1500 tons is 1/36~1/42 which is greater than that of various cargo ships recorded as 1.45~1.75. 4. On speed-length ratio of purse seiners. 111 Dong-a has the biggest value 2.94 the class of 130 tons has 2.52 the class of between 75 and 100 tons has 2.30~2.35 and the class of between 500 and 1500 tons has 1.99~2.05. 5. Turning circle of stern trawlers Pusan 404 and Haelim 3 are measured as below according to rudder angles 5$^{\circ}$, 15$^{\circ}$, 25$^{\circ}$ and 35$^{\circ}$ respectively. Advances are 11.3~13.6, 6.0~7.1, 3.6~4.8 and 2.5~3.5 times of LPP respectively. Tactial diameters are 15.2~18.6, 6.9~8.0, 4.2~4.9 and 2.9~3.5 times of LPP. Purse seiner 111 Dong-a with rudder angle 35$^{\circ}$ has a good yaw with quick responsibility since its advance is 2.2~2.3 times of LPP and since its tactial diameter is 2.0~2.1 times of LPP. 6. In full ahead going of purse seiner 111 Dong-a, it takes about 2 minutes and 10.6 times of LPP from the reverse turning its engine into full astern to the ship speed 0. In its full astern going, it takes about 1 minute and 5.1 times of LPP from the reverse turning its engine into full ahead to the ship speed 0. In its full ahead going, it takes about 2 minutes and 50 seconds and 12.3 times of LPP from stopping its engine to the dead slow ahead speed 3.2 knots.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.110-114
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• 2012

Purpose : PET/CT is widely used for early checking up of cancer and following up of pre and post operation. Image reconstruction method is advanced with mechanical function. We want to evaluate image quality of each reconstruction program based on time of flight (TOF). Materials and Methods : After acquiring phantom images during 2 minutes with Gemini TF (Philips, USA), Biograph mCT (Siemens, USA) and Discovery 690 (GE, USA), we reconstructed image applied to Astonish TF (Philips, USA), ultraHD PET (Siemens, USA), Sharp IR (GE, USA) and not applied. inside of Flangeless Esser PET phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 1.11 kBq/ml (30 Ci/ml) and 4 hot inserts (8. 12. 16. 25 mm) were filled with 8.88 kBq/ml (240 ${\mu}Ci/ml$) the ratio of background activity and hot inserts activity was 1 : 8. Inside of triple line phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 37 MBq/ml (1 mCi). Three of lines were filled with 0.37 MBq (100 ${\mu}Ci$). Contrast ratio and background variability were acquired from reconstruction image used Flangeless Esser PET phantom and resolution was acquired from reconstruction image used triple line phantom. Results : The contrast ratio of image which was not applied to Astonish TF was 8.69, 12.28, 19.31, 25.80% in phantom lid of which size was 8, 12, 16, 25 mm and it which was applied to Astonish TF was 6.24, 13.24, 19.55, 27.60%. It which was not applied to ultraHD PET was 4.94, 12.68, 22.09, 30.14%, it which was applied to ultraHD PET was 4.76, 13.23, 23.72, 31.65%. It which was not applied to SharpIR was 13.18, 17.44, 28.76, 34.67%, it which was applied to SharpIR was 13.15, 18.32, 30.33, 35.73%. The background variability of image which was not applied to Astonish TF was 5.51, 5.42, 7.13, 6.28%. it which was applied to Astonish TF was 7.81, 7.94, 6.40 6.28%. It which was not applied to ultraHD PET was 6.46, 6.63, 5.33, 5.21%, it which was applied to ultraHD PET was 6.08, 6.08, 4.45, 4.58%. It which was not applied to SharpIR was 5.93, 4.82, 4.45, 5.09%, it which was applied to SharpIR was 4.80, 3.92, 3.63, 4.50%. The resolution of phantom line of which location was upper, center, right, which was not applied to Astonish TF was 10.77, 11.54, 9.34 mm it which was applied to Astonish TF was 9.54, 8.90, 8.88 mm. It which was not applied to ultraHD PET was 7.84, 6.95, 8.32 mm, it which was applied to ultraHD PET was 7.51, 6.66, 8.27 mm. It which was not applied to SharpIR was 9.35, 8.69, 8.99, it which was applied to SharpIR was 9.88, 9.18, 9.00 mm. Conclusion : Image quality was advanced generally while reconstruction program which is based on time of flight was used. Futhermore difference of result compared each manufacture reconstruction program showed up, however this is caused by specification of instrument of each manufacture and difference of reconstruction algorithm. Therefore we need further examination to find out appropriate reconstruction condition while using reconstruction program used for advance of image quality.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• The Korean Journal of Nuclear Medicine
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• v.33 no.3
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• pp.316-326
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• 1999

Purpose: We evaluated the feasibility of extracting pure left ventricular blood pool and myocardial time-activity curves (TACs) and of generating factor images from human dynamic N-13 ammonia PET using factor analysis. The myocardial blood flow (MBF) estimates obtained with factor analysis were compared with those obtained with the user drawn region-of-interest (ROI) method. Materials and Methods: Stress and rest N-13 ammonia cardiac PET imaging was acquired for 23 min in 5 patients with coronary artery disease using GE Advance tomograph. Factor analysis generated physiological TACs and factor images using the normalized TACs from each dixel. Four steps were involved in this algorithm: (a) data preprocessing; (b) principal component analysis; (c) oblique rotation with positivity constraints; (d) factor image computation. Area under curves and MBF estimated using the two compartment N-13 ammonia model were used to validate the accuracy of the factor analysis generated physiological TACs. The MBF estimated by factor analysis was compared to the values estimated by using the ROI method. Results: MBF values obtained by factor analysis were linearly correlated with MBF obtained by the ROI method (slope = 0.84, r = 0.91), Left ventricular blood pool TACs obtained by the two methods agreed well (Area under curve ratio: 1.02 ($0{\sim}1min$), 0.98 ($0{\sim}2min$), 0.86 ($1{\sim}2min$)). Conclusion: The results of this study demonstrates that MBF can be measured accurately and noninvasively with dynamic N-13 ammonia PET imaging and factor analysis. This method is simple and accurate, and can measure MBF without blood sampling, ROI definition or spillover correction.

• Investigative Magnetic Resonance Imaging
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• v.5 no.2
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• pp.138-148
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• 2001

Purpose : Within a clinically acceptable time frame, we obtained the high resolution MR images of the human brain, knee, foot and wrist from 3T whole-body MRI system which was equipped with the world first 37 active shield magnet. Materials and Methods : Spin echo (SE) and Fast Spin Echo (FSE) images were obtained from the human brain, knee, foot and wrist of normal subjects using a homemade birdcage and transverse electromagnetic (TEM) resonators operating in quadrature and tuned to 128 MHz. For acquisition of MR images of knee, foot and wrist, we employed a homemade saddle shaped RF coil. Topical common acquisition parameters were as follows: matrix=$512{\times}512$, field of view (FOV) =20 cm, slice thickness = 3 mm, number of excitations (NEX)=1. For T1-weighted MR images, we used TR = 500 ms, TE = 10 or 17.4 ms. For T2-weighted MR images, we used TR=4000 ms, TE = 108 ms. Results : Signal to noise ratio (SNR) of 3T system was measured 2.7 times greater than that of prevalent 1.5T system. MR images obtained from 3T system revealed numerous small venous structures throughout the image plane and provided reasonable delineation between gray and white matter. Conclusion The present results demonstrate that the MR images from 3T system could provide better diagnostic quali\ulcorner of resolution and sensitivity than those of 1.5T system. The elevated SNR observed in the 3T high field magnetic resonance imaging can be utilized to acquire images with a level of resolution approaching the microscopic structural level under in vivo conditions. These images represent a significant advance in our ability to examine small anatomical features with noninvasive imaging methods.

• Journal of Korean Physical Therapy Science
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• v.9 no.1
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• pp.69-80
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• 2002

The objective of research provides the physical therapy of good quality to the patients to search for the problem pant against a physical therapy accident and it simultaneously respects physical therapy company law, the possibility of preparing a system defensive ability in order to be. The data were collected from 2000 October 1 to December 30th, and analyzed by a frequency and a percentage, oneway ANOVA, Scheffe method, $x^2$ official approvals. Conclusion (1) the accident where the patient falls from inside the treatment 'room is many and occasionally' 29.3% (63 people) with was many most. (2) Because of a mistake by a part-time therapist in holiday or a colleague therapist to do, the fracture or bum accident happens 12.5% (27 people), by a assist nurse due to more showed 12.1% (26 people) experience degree in the patient. (3) From physical therapy process breakdown of the medical treatment machinery and tools or it is in malfunction to do and the experience which has a failure to physical therapy is one enemy 68.1% (147 people) was in item. Also it treats and the patient or in the protector it sends an explanation in advance not to be, the experience which it enforces 50% (108 people), of service hour treatment equipment the medical treatment directives broad way of the doctor is accurate in insufficiency and does not enforce the experience is 45.4% (98 people), the patient whom I am treating Hot Pack (electricity has pack inclusion) with to do, the art dealer (over at 1 buffoonery) the experience which it puts on 27.1% (58 people), The patient whom I am treating is the electrotherapy flag (electricity has pack exclusion) with to do, the art dealer (1 degree art dealer over) the experience which it puts on 16.3% (35 people), the experience boat song the patient against a fracture from physical therapy process 9 person (4.2%) was visible an experience degree. (4) With hospital infection to do, from the patient the experience and the therapist which receive a problem proposal were caused by with hospital infection and the answer back regarding the experience which tries to receive a treatment appeared 6% (13 people), 42% (9 people) with each. (5) It listened to the treatment hour patient or the appeal of the protector and especially it does not appear to be being important it was not and and the management which is special it did not take, also the experience where the condition of the patient is deteriorated after that was 10.3% (22 people). (6) The condition or state of the patient does not agree with the medical treatment instruction of the doctor not to be, amendment one experience was 67.5% (145 people). (7) The experience degree of the physical therapy accident which relates with physical therapy recording and a secret maintenance 59.7% (129 people) 'is many and occasionally it is,' it showed an answer back and e it showed a most high accident experience degree. (8) The business overweight of physical therapy company 43.3% (93 people) with was high most from recognition degree of the physical therapy company against a physical therapy accident. (9) Against the question which asks the responsibility subject matter of physical therapy accident the whole answer back volition 42.8% did it is a joint responsibility where the multi person relates. (10) The accident occurs most the hour unit which plentifully in the afternoon 64.3% (133 people) with appeared from the recognition degree against the frequency hour unit of physical therapy accident. (11) Physical therapy it bought and after the various medical treatment accident which relates against the attitude of the, patient side against the physical therapy company it understood and trillion it was many most with 33.3% to be finished. (12) After physical therapy accident the management against the physical therapy company of the hospital authorities concerned above all do not experience 70.6% (149 people), from event right and wrong submission 22.7% (48 people), warning management 2.8% (6 people), the event report requirement and money compensation were each 0.5% (1 person). (13) As the prevention book of physical therapy accident most it is important, the fact which it thinks that, the persons supplement of physical therapy company 58.8% (127 people) with was high most. (14) It related with a physical therapy accident and the medical law 43.5%, civil law 23.9%, was visible the answer back ratio of the criminal law 13.7% from the degree which probably is a relation law.

• Progress in Medical Physics
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• v.12 no.2
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• pp.113-124
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• 2001

For veterinary imaging diagnosis, we obtained MR images of the canine brain, spine, kidney and pelvis from 3T MRI system which was equipped with the world first 3T active shield magnet. Spin echo (SE) and fast Spin Echo (FSE) images were obtained from the canine brain, spine, kidney and pelvis of normal and sick dogs using a homemade birdcage and transverse electromagnetic (TEM) resonators operating in quadrature and tuned to 128 MHz. In addition, we employed a homemade saddle shaped RF coil. Typical common acquisition parameters were as follows: matrix=512$\times$512, field of view (FOV)=20cm, slice thickness=3 w, number of excitations (NEX)=1. For T1-weighted MR images, we used TR=500 ms, TE=10 or 17.4 ms. For T2-weighted MR images, we used TR=4000 ms, TE=108 ms. Signal to noise ratio (SNR) of 3T system was measured 2.7 times greater than that of prevalent 1.57 system. The high resolution images acquired in this study represent more than a 4-fold increase in in-plane resolution relative to conventional images obtained with a 20 cm field of view and a 5 mm slice thickness. MR images obtained from 3T system revealed numerous small venous structures throughout the image plane and provided reasonable delineation between gray and white matter The present results demonstrate that the MR images from 3T system could provide better diagnostic quality of resolution and sensitivity than those of 1.5T system. The elevated SNR observed in the 3T high field magnetic resonance imaging can be utilized to acquire images with a level of resolution approaching the microscopic structural level under in vivo conditions. These images represent a significant advance in our ability to examine small anatomical features with noninvasive imaging methods. Moreover, MRI technique could begin to apply for veterinary medicine in Korea.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.