• Asia pacific journal of information systems
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• v.19 no.3
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• pp.125-150
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• 2009

RFID(Radio Frequency IDentification) is a wireless frequency of recognition technology that can be used to recognize, trace, and identify people, things, and animals using radio frequency(RF). RFID will bring about many changes in manufacturing and distributions, among other areas. In accordance with the increasing importance of RFID techniques, great advancement has been made in RFID studies. Initially, the RFID research started as a research literature or case study. Recently, empirical research has floated on the surface for announcement. But most of the existing researches on RFID adoption have been restricted to a dichotomous measure of 'adoption vs. non-adoption' or adoption intention. In short, RFID research is still at an initial stage, mainly focusing on the research of the RFID performance, integration, and its usage has been considered dismissive. The purpose of this study is to investigate which factors are important for the RFID adoption and implementation with organizational resources. In this study, the organizational resources are classified into either finance resources or IT knowledge resources. A research model and four hypotheses are set up to identify the relationships among these variables based on the investigations of such theories as technological innovations, adoption stage, and organizational resources. In order to conduct this study, a survey was carried out from September 27, 2008 until October 23, 2008. The questionnaire was completed by 143 managers and workers from physical distribution and manufacturing companies related to the RFID in South Korea. 37 out of 180 surveys, which turned out unfit for the study, were discarded and the remaining 143(adoption stage 89, implementation stage 54) were used for the empirical study. The statistics were analyzed using Excel 2003 and SPSS 12.0. The results of the analysis are as follows. First, the adoption stage shows that perceived benefits, standardization, perceived cost savings, environmental uncertainty, and pressures from rival firms have significant effects on the intent of the RFID adoption. Further, the implementation stage shows that perceived benefits, standardization, environmental uncertainty, pressures from rival firms, inter-organizational cooperation, and inter-organizational trust have significant effects on the extent of the RFID use. In contrast, inter-organizational cooperation and inter-organizational trust did not show much impact on the intent of RFID adoption while perceived cost savings did not significantly affect the extent of RFID use. Second, in the adoption stage, financial issues had adverse effect on both inter-organizational cooperation and the intent against the RFID adoption. IT knowledge resources also had a deterring effect on both perceived cost savings and the extent of the RFID adoption. Third, in the implementation stage, finance resources had a moderate effect on environmental uncertainty and extent of RFID use while IT knowledge resources had also a moderate effect on perceived cost savings and the extent of the RFID use. Limitations and future research issues can be summarized as follows. First, it is difficult to say that the sample is large enough to be representative of the population. Second, because the sample of this study was conducted among manufacturers only, it may be limited in analyzing fully the effect on the industry as a whole. Third, in consideration of the fact that the organizational resources in the RFID study require a great deal of researches, this research may deem insufficient to fulfill the purpose that it initially set out to achieve. Future studies using performance research are, therefore, needed to help better understand the organizational level of the RFID adoption and implementation.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.365-374
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• 2015

The regular quality assurance (RQA) of X-ray images is essential for maintaining a high accuracy of diagnosis. This study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) of a computed radiography (CR) system for various periods of use from 2006 to 2015. We measured the pre-sampling MTF using the edge method and RQA 5 based on commission standard international electro-technical commission (IEC). The spatial frequencies corresponding to the 50% MTF for the CR systems in 2006, 2009, 2012 and 2015 were 1.54, 1.14, 1.12, and $1.38mm^{-1}$, respectively and the10% MTF for 2006, 2009, 2012, and 2015 were 2.68, 2.44, 2.44, and $2.46mm^{-1}$, respectively. In the NPS results, the CR systems showed the best noise distribution in 2006, and with the quality of distributions in the order of 2015, 2009, and 2012. At peak DQE and DQE at $1mm^{-1}$, the CR systems showed the best efficiency in 2006, and showed better efficiency in order of 2015, 2009, and 2012. Because the eraser lamp in the CR systems was replaced, the image quality in 2015 was superior to those in 2009 and 2012. This study can be incorporated into used in clinical QA requiring performance and evaluation of the performance of the CR systems.

• Radiation Oncology Journal
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• v.11 no.2
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• pp.249-258
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• 1993

Between March 1983 and December 1989, ninety-six patients with intracranial glioma were treated in the Department of Therapeutic Radiology, Kangnam St. Mary's Hospital, Catholic University Medical College. We retrospectively reviewed each case to evaluate variable factors influencing the treatment results and to develop an optimal therapy Policy. Median follow-up is 57 months (range: 31~133 months). Of the 96 patients, 60 $(63\%)$ were males and 36 $(37\%)$ were females. Ages ranged from 3 to 69 years (median 42 years). The most common presenting symtoms were headeche $(67\%)$ followed by cerebral motor and sensory discrepancy $(54\%),$ nausea and vomiting $(34\%),$ seizure $(19\%),$ mental change $(10\%)$ and memory and calculation impairment $(8\%).$ Eighty five $(88.5\%)$ patients all, except 11 $(11.5\%)$ brain stem lesions, were biopsy proven intracranial glioma. The distribution by histologic type was 64 astrocytomas $(75\%),$ 4 mixed oligoastrocytomas $(5\%),$ and 17 oligodendrogliomas $(20\%).$ Fourty nine patients $(58\%$ were grade I, II histology and 36 $(42\%)$ patients were grade III, IV histology. Of the 96 patients, 64 $(67\%)$ recieved postoperative RT and 32 $(33\%)$ were treated with primary radiotherapy. Gross total resection was peformed in 14 $(16\%)$ patients, subtotal resection En 29 $(34\%),$ partial resection in 21 $(25\%),$ and biopsy only in 21 $(25\%).$ Median survival time was 53 months (range 2~ 133 months), and 2- and, 5-year survival rate were $69\%,49\%$ respectively. 5-year survival rate by histologic grade was grade I, $70\%,$ grade II, $58\%,$ grade III, $28\%,$ and grade IV, $15\%.$ Multivariated analysis demonstrate that age at diagnosis (p=0.0121), Karnofsky performance Status (KPS) (p=0.0002), histologic grade (p=0.0001), postoperative radiation therapy (p=0.0278), surgical extent (p =0.024), cerebellar location of tumor (p=0.0095) were significant prognostic factors influencing on survival.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.563-579
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• 2016

This study seeks educational implication by analyzing research papers dealing with science assessment in the most recent 30 years in Korea. The main purpose of the study is to analyze the trends in published papers on science assessment, their purpose, methodology, and key words, especially concentrating on the cognitive and affective domains. We selected 273 research articles and categorized them by research object, subject, methodology, and contents. To examine the factors that affect the research trend, we also tried to contextualize papers' theme in terms of changes in national curriculum and assessment system during the contemporary period. As a result, an overall research trend reflects changes in science curriculum and assessment events such as implementation of college scholastic ability test or performance assessment. There is an unequal distribution in various aspects of the researches, showing a superiority in cognitive domains than the affective ones. By using standardized data obtained through the national and international assessment of educational achievement in science, quantitative researches were superior to qualitative ones. Studies on cognitive domain use variously written- and performance-based tests, whereas most studies of the affective ones prefer written tests. Applied research and evaluation research are predominant comparing to basic ones, which most of the research methodology is based on statistics. Lastly, we found out that key words and subjects tend to be subdivided and detailed rather than general and comprehensive, as time goes on. Such trend will be helpful to elaborate and refine assessment tools that have been regarded as a problem.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.12
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• pp.715-732
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• 2009

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2008. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) Research trends in thermal and fluid engineering have been surveyed in the categories of general fluid flow, fluid machinery and piping, new and renewable energy, and fire. Well-developed CFD technologies were widely applied in developing facilities and their systems. New research topics include fire, fuel cell, and solar energy. Research was mainly focused on flow distribution and optimization in the fields of fluid machinery and piping. Topics related to the development of fans and compressors had been popular, but were no longer investigated widely. Research papers on micro heat exchangers using nanofluids and micro pumps were also not presented during this period. There were some studies on thermal reliability and performance in the fields of new and renewable energy. Numerical simulations of smoke ventilation and the spread of fire were the main topics in the field of fire. (2) Research works on heat transfer presented in 2008 have been reviewed in the categories of heat transfer characteristics, industrial heat exchangers, and ground heat exchangers. Research on heat transfer characteristics included thermal transport in cryogenic vessels, dish solar collectors, radiative thermal reflectors, variable conductance heat pipes, and flow condensation and evaporation of refrigerants. In the area of industrial heat exchangers, examined are research on micro-channel plate heat exchangers, liquid cooled cold plates, fin-tube heat exchangers, and frost behavior of heat exchanger fins. Measurements on ground thermal conductivity and on the thermal diffusion characteristics of ground heat exchangers were reported. (3) In the field of refrigeration, many studies were presented on simultaneous heating and cooling heat pump systems. Switching between various operation modes and optimizing the refrigerant charge were considered in this research. Studies of heat pump systems using unutilized energy sources such as sewage water and river water were reported. Evaporative cooling was studied both theoretically and experimentally as a potential alternative to the conventional methods. (4) Research papers on building facilities have been reviewed and divided into studies on heat and cold sources, air conditioning and air cleaning, ventilation, automatic control of heat sources with piping systems, and sound reduction in hydraulic turbine dynamo rooms. In particular, considered were efficient and effective uses of energy resulting in reduced environmental pollution and operating costs. (5) In the field of building environments, many studies focused on health and comfort. Ventilation. system performance was considered to be important in improving indoor air conditions. Due to high oil prices, various tests were planned to examine building energy consumption and to cut life cycle costs.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Atmosphere
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• v.32 no.2
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• pp.87-101
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• 2022

In this paper, the performance improvement for the new KMA's Climate Prediction System (GloSea6), which has been built and tested in 2021, is presented by assessing the bias distribution of basic variables from 24 years of GloSea6 hindcasts. Along with the upgrade from GloSea5 to GloSea6, the performance of GloSea6 can be regarded as notable in many respects: improvements in (i) negative bias of geopotential height over the tropical and mid-latitude troposphere and over polar stratosphere in boreal summer; (ii) cold bias of tropospheric temperature; (iii) underestimation of mid-latitude jets; (iv) dry bias in the lower troposphere; (v) cold tongue bias in the equatorial SST and the warm bias of Southern Ocean, suggesting the potential of improvements to the major climate variability in GloSea6. The warm surface temperature in the northern hemisphere continent in summer is eliminated by using CDF-matched soil-moisture initials. However, the cold bias in high latitude snow-covered area in winter still needs to be improved in the future. The intensification of the westerly winds of the summer Asian monsoon and the weakening of the northwest Pacific high, which are considered to be major errors in the GloSea system, had not been significantly improved. However, both the use of increased number of ensembles and the initial conditions at the closest initial dates reveals possibility to improve these biases. It is also noted that the effect of ensemble expansion mainly contributes to the improvement of annual variability over high latitudes and polar regions.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.