• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.1
• /
• pp.82-89
• /
• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Asia Marketing Journal
• /
• v.14 no.1
• /
• pp.83-98
• /
• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.

• Journal of Korean Society of Transportation
• /
• v.30 no.1
• /
• pp.7-18
• /
• 2012

This study suggested a method of forecasting market-share of each mode after introducing new urban rail transit lines. The study reflected the observed market share of presently operating urban rail transit into forecasting process in order to improve accuracy in predicting market share of each modes. For more realistic representation of the forecasting model, we categorized O/D pairs according to attributes of trip distance, access time and number of transfers. The analysis results of traveler's mode choice behavior with observed data showed that the trip distances are longer, the share of urban rail tends to be higher, and that the number of transfers is fewer and the access times are lesser, the share of urban rail also tends to be higher. Then, incremental logit model was used in estimating mode choice probabilities for O/D pairs along with rail transit lines while utilizing observed market shares of each modes and differences in transit service level. As the next step, the market share of rail transit after introducing new rail transit lines was forecasted by using incremental logit model with the intial share values calculated the previous analysis step. It also reflected changes in level of service for automobile in highway due to changes in highway systems and changes in mode shares after introducing new lines of rail transit. It can be expected that the proposed method would more realistically duplicates phenomena of mode choice behavior for rail transit and that it would be more theoretically logical than the typical existing methods using SP data and incremental logit model or using addictive logit model in this country.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.39-70
• /
• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Archives of design research
• /
• v.19 no.1 s.63
• /
• pp.253-262
• /
• 2006

Research of color has been developed and also has raised consumer desire through changing from a tool to pursue curiosity or beauty to a tool creating effects in the 20th century. People have been interested in colors as a dynamic expression of results since the color TV appeared. The meaning of colors has been recently diversified as the roles of colors became important to the emotional aspects of design. While auto colors have developed along with such changes of the times, black led the color trend during the first half of the 20th century from 1900 to 1950, a transitional period of economic growth and world war. Since then, automobile production has increased apace with the rapid economic growth throughout the world and automobiles became the most expensive item out of the goods that people use. Accordingly, increasing production induced facility investment in mass production and a technology leveling was achieved. Auto manufacturing processes are very complicated, auto makers gradually recognized that software changes such as to colors or materials was an easier way for the improvement of brand identity as opposed to hardware changes such as the mechanical or design components of the body. Color planning and development systems were segmented in various aspects. In the segmentation issue, pigment technology and painting methods are important elements that have an influence on body colors and have a higher technical correlation with colors than in other industries. In other words, the advanced mixture of pigments is creating new body colors that have not existed previously. This diversifies the painting structure and methods and so maximizes the transparency and depth of body colors. Thus, body colors that are closely related to technical factors will increase in the future and research on color preferences by region have been systemized to cope with global competition due to the expansion and change of auto export regions.

• Journal of Global Scholars of Marketing Science
• /
• v.18 no.3
• /
• pp.27-57
• /
• 2008

Many consumers enjoy the challenge of purchasing a brand that matches well with their own values and personalities (for example, Ko et al., 2008; Ko et al., 2006). Therefore, the personalities of consumers can impact on the final selection of a brand and its brand personality in two ways: first, the consumers may incline to purchase a brand or a product that reflects their own personalities; second, consumers tend to choose a company that has similar brand personalities to those brands that are being promoted. Therefore, the objectives of this study are following: 1. Is there any empirical relationship between a consumer's personality and the personality of a brand that he or she chooses? 2. Can a corporate brand be differentiated by the brand personality? In short, consumers are more likely to hold favorable attitudes towards those brands that match their own personality and will most probably purchase those brands matching well with their personality. For example, Matzler et al. (2006) found that extraversion and openness were positively related to hedonic product value; and that the personality traits directly (openness) and indirectly (extraversion, via hedonic value) influenced brand effects, which in turn droved attitudinal and purchase loyalty. Based on the above discussion, the following hypotheses are proposed: Hypothesis 1: the personality of a consumer is related to the brand personality of a product/corporate that he/she purchases. Kuksov (2007) and Wernerfelt (1990) argued that brands as a symbolic language allowed consumers to communicate their types to each other and postulated that consumers had a certain value of communicating their types to each other. Therefore, how brand meanings are established, and how a firm communicate with consumers about the meanings of the brand are interesting topics for research (for example, Escalas and Bettman, 2005; McCracken, 1989; Moon, 2007). Hence, the following hypothesis is proposed: Hypothesis 2: A corporate brand identity is differentiated by the brand personality. And there are significant differences among companies. A questionnaire was developed for collecting empirical measures of the Big-Five personality traits and brand personality variables. A survey was conducted to the online access panel members through the Internet during December 2007 in Korea. In total, 500 respondents completed the questionnaire, and considered as useable. Personality constructs were measured using the Five-factor Inventory (NEO-FFI) scale and a total of 30 items were actually utilized. Brand personality was measured using the five-dimension scale developed by Aaker (1997). A total of 17 items were actually utilized. The seven-point Likert-type scale was the format of responses, for example, from 1 indicating strongly disagreed to 7 for strongly agreed. The Analysis of Moment Structures (AMOS) was used for an empirical testing of the model, and the Maximum Likelihood Estimation (MLE) was applied to estimate numerical values for the components in the model. To diagnose the presence of distribution problems in the data and to gauge their effects on the parameter estimates, bootstapping method was used. The results of the hypothesis-1 test empirically show that there exit certain causality relationship between a consumer's personality and the brand personality of the consumer's choice. Thus, the consumer's personality has an impact on consumer's final selection of a brand that has a brand personality matches well with their own personalities. In other words, the consumers are inclined to purchase a brand that reflects their own personalities and tend to choose a company that has similar brand personalities to those of the brand being promoted. The results of this study further suggest that certain dimensions of the brand personality cause consumers to have preference to certain (corporate) brands. For example, the conscientiousness, neuroticism, and extraversion of the consumer personality have positively related to a selection of "ruggedness" characteristics of the brand personality. Consumers who possess that personality dimension seek for matching with certain brand personality dimensions. Results of the hypothesis-2 test show that the average "ruggedness" attributes of the brand personality differ significantly among Korean automobile manufacturers. However, the result of ANOVA also indicates that there are no significant differences in the mean values among manufacturers for the "sophistication," "excitement," "competence" and "sincerity" attributes of the corporate brand personality. The tight link between what a firm is and its corporate brand means that there is far less room for marketing communications than there is with products and brands. Consequently, successful corporate brand strategies must position the organization within the boundaries of what is acceptable, while at the same time differentiating the organization from its competitors.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.5
• /
• pp.2021-2031
• /
• 2013

Due to the rapid economic growth within the nation, the quality of life of individuals have improved dramatically. The scope of living activities of individuals have also extended, resulting in a rapidly increasing demand for automobiles. The number of vehicles registered in Korea is rapidly increasing and will reach 188.71 million as of December 2012. Compared to the registered residents of 50.94 million provided by the Ministry of Public Administration and Security, the registered population reflects about 4 people per every automobile. Due to the high demands for vehicles, the demands for parking lots in collective housing and businesses are also increasing. In reality, the current state of expansion of parking lots are underground, due to the limited available space on ground level. Specifically, the slope of a parking lot cannot exceed 17% linear slope and 14% curved slope according to the 'parking lot laws', however studies show that the driver feels at risk for safety when stopped on the parking ramp while driving in the parking lot. This study seeks to examine the suitability of parking lot ramps, concerning the safety aspects of the driver. First, the ramp type was categorized as linear or curved, then test drives were performed based on variations of slopes, slant distances, directions and points. A survey was administered to the driver after the completion of the test drive, in order to element design for an ideal ramp. In the case of curved ramp, the results of the estimate suggests a counterclockwise, slope at a maximum of 12% incline. The maximum slope for a linear ramp was analyzed to be between 13~14%, suggesting that slope greater than 15% need to be eliminated. In conclusion, it is anticipated that the element design parking ramp reported in this study will help to serve as a reference for future parking lot related guidelines, and provide cost effective traffic safety mechanisms in future parking lot businesses to follow.

• Economic and Environmental Geology
• /
• v.40 no.3 s.184
• /
• pp.263-275
• /
• 2007

The purpose of this study was to show the pollution characteristics of hazardous elements from roadside dust in the Gwangju city. We collected 47 samples from November to December in 2004 and separated four groups such as residential area, industrialized area, downtown area and heavy traffic area fer characteristics comparison on hazardous elements. Roadside dust mostly consisted of quartz, albite, microcline, muscovite in XRD analysis. Content of hazardous elements varied: As $3.4{\sim}11.9 ppm$, Cd $0.2{\sim}28.2 ppm$, Co $32{\sim}526 ppm$, Cr $25{\sim}526 ppm$, Cu $11{\sim}375 ppm$, Ni $14{\sim}247 ppm$, Pb $13{\sim}413 ppm$ and Zn $101{\sim}972 ppm$. Average contents of hazardous elements of Zn>Cu>Pb>Cr>Co>Ni>Cd. Content of hazardous elements was low in residential area, whereas that of heavy metal was much the same in both in heavy traffic area. Content of hazardous elements such as Cd, Co, Cr, Cu, Ni, Pb, Zn was found to be particularly high in industrialized area. According to these results it was possible to presume that industrialized area was affected by industry activity such as machinery, petrochemical, automobile and electronics industry. The SEM analysis, detected Pb, Cr, Ni, and Fe particles in samples of industrialized area contaminated by industry activity. The correlation coefficient table resulted from the samples of roadside dust showed that there was same direction increase of content between elements. In other words, when the content of Cd increase, Cr and Ni increase, as Cr increase, Cu and Ni increase, as Cu increase Ni increase and Pb increase Zn increase. Based on these results it was possible to predict and interpret similar contamination patterns in this study.

• Applied Chemistry for Engineering
• /
• v.5 no.5
• /
• pp.808-818
• /
• 1994

Effects of ceria additive on the activity and thermal aging behavior of supported Ru catalysts were investigated using Ru/${\gamma}$-$Al_2O_3$and Ru/$CeO_2$-${\gamma}$-$Al_2O_3$. The catalysts were characterized by $^{129}Xe$-NMR and $H_2$ chemisorption. The cataltic activity for conversion of CO, HC and $NO_x$ was measured using simulated automobile engine exhausts under lean, rich and stoichiometric conditions. For both fresh and aged catalysts, Ru/$CeO_2$-${\gamma}$-$Al_2O_3$ was more active than Ru/${\gamma}$-$Al_2O_3$ for all three pollutants. Results of $^{129}Xe$-NMR and $H_2$ chemisorption indicated that sintering of Ru particles occurred to the same extent for both catalysts during the thermal aging process. After thermal aging at 673K, however, the catalytic activity of the aged Ru/$CeO_2$-${\gamma}$-$Al_2O_3$ was substantially higher than that of the fresh one, while the activity of Ru/${\gamma}$-$Al_2O_3$ decreased after the thermal aging. This finding may suggest new active sites were created during the thermal aging, probably in the vicinity of the interface between Ru and Ce. For more quantitative investigation of the effect of a cation such as Ce on the thermal aging of Ru metal particles, Ru catalysts supported on cation-exchanged Y-zeolites were used as the model catalysts. The results indicated that when Ba, Ca, La, Y or Ce was used for the cation exchange, the exchanged cation did not affect the thermal aging behavior of Ru in Y-zeolite, as evidenced by $^{129}Xe$-NMR and EXAFS.

• 대한공업교육학회지
• /
• v.38 no.1
• /
• pp.221-238
• /
• 2013

The purpose of this study was to revise and reorganize the direction of the department of ${\bigcirc}{\bigcirc}$Industrial High School though analysis of manufacturing status and workforce demand in Chungnam province focused on the Geumsan Area. In the study, ${\bigcirc}{\bigcirc}$Industrial High School of the status and actual conditions were identified through interview, literature review and data analysis. Surveys of the school teachers, parents and students was conducted in order to investigate the awareness of renaming and reorganization of school departments, curriculum revision of the school. Statistical data was collected and analyzed in order to figure out manufacturing industry and its workforce demand of Chungnam Province in Korea. Findings of the study were as follows: Small and medium enterprises of manufacturing industry have been developed a lot in Geumsan Area in Chungnam province. Four major industries including (1) automobile parts, (2) electronic and information equipment, (3) Cutting edge culture, and (4) Agricultural-livestock and bio are intensively fostered as regional strategic industries in the Chungnam province. The manufacturing industry has a 33.6-percent, and then service-mining and manufacturing industry has a 80.0-percent of total number of employee in Geumsan Area. It is expected that industrial workforce demand of Geumsan Area come out of manufacturing and service-mining industrial sector. The following is recommended for the school curriculum revision: (1) focussing on mechanical control for the revision of computer applying mechanical department, (2) focussing on automation electric equipment for the revision of electric control department, (3) focussing on food process control for revising of bio-food industrial department. It's helpful to make a progress of the school that establish identification of industrial specialized high school as an institution of vocational education at the secondary level through supplying qualified workforce to Manufacturing industry in Chungnam Province.