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As an efficient file system for SSD(Solid State Drive), F2FS is employed in the kernel of Linux operating system. F2FS applies various methods to improve performance by reflecting the characteristics of flash memory. One of them is improvement of the index structure that contains addresses of data blocks for each file. This paper presents a method for further improving performance by modifying the index structure of F2FS. F2FS manages all index blocks as logical numbers, and an address mapping table is used to find the physical block addresses of index blocks on flash memory. This paper shows performance improvement by applying logical numbers to the last level index blocks only. The count of mapping table search for a data block access is reduced to 1~2 from 1~4.

In this paper, we propose a plan to design and utilize the RDBS(Record Block Data file management System) so that data can be recovered when data files in the Combat Management System are mismatched. The CMS(Combat Management System) manages the same files in multiple IPN(Infomation Processing Node) repositories to support multiplexing. However, mismatches in data files can occur due to equipment maintenance or user immaturity. The existing CMS does not manage the history of changes in data files, and when a mismatch occurs, data file were synchronized based on the latest date. But, It is difficult to say that files with the latest date have the highest reliability, and once the file synchronization has progressed, it cannot be recovered with pre-synchronization data. To solve this problem, data was stored and synchronized in units of record blocks using RDBS proposed in this paper, and the Rsync algorithm was used to reduce the overhead of file synchronization due to units of record blocks. SW applied with RDBS was tested for performance in a simulated environment, and it was confirmed that it could be applied to CMS through normal operation confirmation.

In this paper, ED-H algorithm, an optimal real-time scheduling algorithm dealing with the characteristics of the integrated energy harvester system with a capacitor, is extended to satisfy the time constraint under the blackout state which is a deliberate power-off state by an intelligent power management unit adopted in the system. If the power supply system does not have enough energy, it temporarily shuts off the power supply to protect the circuit and capacitor and resumes the supply again when the capacitor is fully charged, which may delay the task execution during these blackout states by calculating the time according to the occurrence of the events. To mitigate the problem, even if task execution is delayed by the original ED-H algorithm, the remaining time of the subsequent time units no longer can afford to delay the execution of the task is predicted in the extended algorithm and the task is forced to be scheduled to meet the time deadline. According to the simulation results, it is confirmed that the algorithm proposed in this paper has a high scheduling performance increase of 0.4% to 7.7% depending on the characteristics of the set of tasks compared to the ED-H.

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

Documents often contain relative time expressions, and it is important to define a schema of the relative time information and develop a system that extracts such information from corpus. In this study, to deal with the relative time expressions, we propose seven additional attributes of timex3: year, month, day, week, hour, minute, and second. We propose a way to represent normalized values of the relative time expressions such as before, after, and count, and also design a set of rules to extract the relative time information from texts. With a new corpus constructed using the new attributes that consists of dialog, news, and history documents, we observed that our rule-set generally achieved 70% accuracy on the 1,041 documents. Especially, with the most frequently appeared attributes such as year, day, and week, we got higher accuracies compared to other attributes. The results of this study, our proposed timex3 attributes and the rule-set, will be useful in the development of services such as question-answer systems and chatbots.

In this paper, we propose a decision tree-based machine learning model that leads to food exchange table renewal by classifying food groups through machine learning for existing food and food data found by web crawling. The food exchange table is the standard for food exchange intake when composing a diet such as diet and diet, as well as patients who need nutritional management. The food exchange table, which is the standard for the composition of the diet, takes a lot of manpower and time in the process of revision through the National Health and Nutrition Survey, making it difficult to quickly reflect food changes according to new foods or trends. Since the proposed technique classifies newly added foods based on the existing food group, it is possible to organize a rapid food exchange table reflecting the trend of food. As a result of classifying food into the proposed model in the study, the accuracy of the food group in the food exchange table was 97.45%, so this food classification model is expected to be highly utilized for the composition of a diet that suits your taste in hospitals and nursing homes.

This paper presents a method for 1:1 verification by comparing the similarity between the given real product image and the drawing image. The proposed method combines two existing CNN-based deep learning models to construct a Siamese Network. After extracting the feature vector of the image through the FC (Fully Connected) Layer of each network and comparing the similarity, if the real product image and the drawing image (front view, left and right side view, top view, etc) are the same product, the similarity is set to 1 for learning and, if it is a different product, the similarity is set to 0. The test (inference) model is a deep learning model that queries the real product image and the drawing image in pairs to determine whether the pair is the same product or not. In the proposed model, through a comparison of the similarity between the real product image and the drawing image, if the similarity is greater than or equal to a threshold value (Threshold: 0.5), it is determined that the product is the same, and if it is less than or equal to, it is determined that the product is a different product. The proposed model showed an accuracy of about 71.8% for a query to a product (positive: positive) with the same drawing as the real product, and an accuracy of about 83.1% for a query to a different product (positive: negative). In the future, we plan to conduct a study to improve the matching accuracy between the real product image and the drawing image by combining the parameter optimization study with the proposed model and adding processes such as data purification.

This paper proposes a method of refining and processing time-series data using Medical Information Mart for Intensive Care (MIMIC-IV) v2.0 data. In addition, the significance of the processing method was validated through a machine learning-based pressure ulcer early warning system using a dataset processed based on the proposed method. The implemented system alerts medical staff in advance 12 and 24 hours before a lesion occurs. In conjunction with the Electronic Medical Record (EMR) system, it informs the medical staff of the risk of a patient's pressure ulcer development in real-time to support a clinical decision, and further, it enables the efficient allocation of medical resources. Among several machine learning models, the GRU model showed the best performance with AUROC of 0.831 for 12 hours and 0.822 for 24 hours.

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

In this study, we aim to design an action game that increases the user experience. In order to increase the immersion of the game, the characteristics of the game used by the user were analyzed, and the systemic and visual characteristics of the game were designed with reference to each characteristic. The proposed method uses Unity 3D to implement an interaction system between objects in the game and is designed in a way that allows users to immerse themselves in the game. To induce immersion through the visual elements of the game, 2D objects and players are placed in a 3D space, and a 2D dynamic light shader is added. It is composed of inter-combat rules and monster behavior pattern collision detection and event detection. The proposed method contained the user experience with the implementation thesis, and showed the game's possibility of leading the user's affordance.

In this paper, we propose a mobile app for personalized rehabilitation exercise coaching and management service using an edge computing-based personalized exercise information collection system. The existing management method that relies on user input information has difficulty in examining the actual possibility of rehabilitation. In this paper, we implement an application that collects movement information along with body joint information through image information analysis based on edge computing at a remote location, measures the time and accuracy of the movement, and provides rehabilitation progress through correct posture information. In addition, in connection with the measurement equipment of the rehabilitation center, the health status can be managed, and the accuracy of exercise information and trend analysis information is provided. The results of this study will enable management and coaching according to self-rehabilitation exercises in a contactless environment.

This study sheds light on the Instagrammable features and viewing experiences of <Accidentally Wes Anderson (AWA)>. A research model and hypotheses are formulated using variables including Experience Quality (EQ), Perceived Value (PV), Attitude toward AWA (ATAWA) and Behavioral Intention toward AWA (BITAWA). Although AWA has strong the Instagrammable features in terms of PV, it provides aesthetic or healing experience rather than a pleasure. PV has a significant correlation with DOS and DOI, but it does not influence on BITAWA. In addition, DOS has a positive impact on the increase in DOI, length of viewing time and intention to upload and share photos on Instagram.

The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

the Naval Combat Management System(CMS) has been installed and used in various ships since its localization, and has been developed by continuously introducing the latest technology. Recently, surface ship CMS have applied server virtualization and desktop virtualization(Virtual Desktop Infra, VDI) technologies among virtualization technologies to increase system stability and limitations on the limited space and weight of ships. On the other hand, submarine CMS do not have virtualization technology applied, so there are limitations in space and weight limitations and CMS efficiency improvement. To this end, this paper proposes a next-generation submarine CMS using Docker-based server virtualization. Through performance analysis between the processor of the existing CMS and the processor to which Docker-based server virtualization was applied, it was confirmed that the method proposed in this paper is applicable to the next-generation submarine CMS.

In this paper, to verify the authenticity of UxNB that assists terrestrial base stations, the solutions for SI (System Information) security presented in 3GPP TR 33.809 are analyzed from the perspective of UxNB. According to the definition of 3GPP (Third Generation Partnership Project), UxNB is a base station mounted on a UAV (Unmanned Aerial Vehicle), is carried in the air by the UAV, and is a radio access node that provides a connection to the UE (User Equipment). Such solutions for SI security can be classified into hash based, MAC (Message Authentication Codes) based, and digital signature based, and a representative solution for each category is introduced one by one. From the perspective of verifying the authenticity of UxNB for each solution, we compare and analyze the solutions in terms of provisioning information and update, security information leakage of UxNB, and additionally required amount of computation and transmission. As a result of the analysis, the solution for verifying the authenticity of the UxNB should minimize the secret information to be stored in the UxNB, be stored in a secure place, and apply encryption when it is updated over the air. In addition, due to the properties of the low computing power of UxNB and the lack of power, it is necessary to minimize the amount of computation and transmission.

In this paper, we propose a design of CCTV enlcosure record management system based on blockchain. Since CCTV video records are transferred to the control center through enclosure, it is very important to manage the enclosure to prevent modulation and damage of the video records. Recently, a smart enclosure monitoring system with real-time remote monitoring and opening and closing state management functions is used to manage CCTV enclosures, but there is a limitation to securing the safety of CCTV video records. The proposed system detect modulated record and recover the record through hash value comparison by distributed stored record in the blockchain. In addition, the integrity verification API is provided to ensure the integrity of enclosure record received by the management server. In order to verify the effectiveness of the system, the integrity verification accuracy and elapsed time were measured through experiments. As a result, the integrity of enclosure record (accuracy: 100%) was confirmed, and it was confirmed that the elapsed time for verification (average: 73 ms) did not affect monitoring.

In this paper, we propose the standardization architecture and weapon-sensor simulator for efficient reliability test in combat system. To reduce man-month of reliability test, application with high dependency on other module is selected and apply FORM. The proposed standardization architecture extracts common, variable elements and design patterns, S.O.L.I.D principles were applied. The proposed weapon-sensor simulator implements essential functions by identifying highly dependent element of other modules and the information from equipment can be directly received without processing by using communication middleware. As a result, it can replace actual ship-mounted equipment. In addition, it is possible to reduce the consumption rate of human resources when perform reliability test and modification time can be shorted.

With the development of mobile devices and global positioning systems, various location-based services can be utilized, which collects user's location information and provides services based on it. In this process, there is a risk of personal sensitive information being exposed to the outside, and thus Geo-indistinguishability (Geo-Ind), which protect location privacy of LBS users by perturbing their true location, is widely used. However, owing to the data perturbation mechanism of Geo-Ind, it is hard to accurately obtain the density distribution of LBS users from the collection of perturbed location data. Thus, in this paper, we aim to develop a novel method which enables to effectively compute the user density distribution from perturbed location dataset collected under Geo-Ind. In particular, the proposed method leverages Expectation-Maximization(EM) algorithm to precisely estimate the density disribution of LBS users from perturbed location dataset. Experimental results on real world datasets show that our proposed method achieves significantly better performance than a baseline approach.

In this paper, we propose a classification method according to the type and characteristics of stablecoins for risk analysis, and analyze the risk factors of each stablecoin based on this classification. First, this paper explains the technologies and ecosystem of blockchain and decentralized finance(DeFi) to understand stablecoins. In addition, the operation principle of the major stablecoins currently released and used is explained for each proposed classification type. Based on this, the risk type and risk factors of each stablecoin are derived. The risk types proposed in this paper are classified as defegging, liquidation, and exploit, and the risk factors are classified as depegging due to reliability of operator, depegging due to reliability of algorithm, depegging due to failure of algorithm, liquidation due to high volatilty and oracle attack. Based on the proposed classification, we analyze the risk factors of major stablecoins currently circulating in the crypto market.

In this paper, we propose an efficient and procedural software patch management phases. Every year, organizations have tens of thousands of known vulnerabilities and spend tens of thousands of hours and millions of dollars or more patching them. Despite these efforts, research has shown that the time it takes for an exploit to appear for a given patched vulnerability is shortening. As various types of organizations such as companies and universities manage patches in inconsistent ways, problems such as security problems, system instability, information leakage and work delay due to patches occur. In this paper, we look at the basics required for software patch management and define the factors to be considered for patch management and the effective steps for patch management. Therefore, this study will be used as a method to efficiently and procedurally execute the organization's patch management policy in the process of updating and patching the software in the organization to a new version as a solution to software function modification and security vulnerability.

The Korean government has made it a major national task to contribute to the revitalization of the creative economy, such as creating new industries and jobs, by encouraging the private opening and utilization of public data. The Korean government is promoting public data quality improvement through activities such as conducting public data quality management level evaluation for high-quality public data retention. However, there is a difference in diagnosis results depending on the understanding and data expertise of users of the public data quality diagnosis tool. Therefore, it is difficult to ensure the accuracy of the diagnosis results. This paper proposes a public data quality management level evaluation domain rule mapping model applicable to validation diagnosis among the data quality diagnosis standards. This increases the stability and accuracy of public data quality diagnosis.

The Naval Combat System operates in multiple software components for efficient functional processing. Data transmission and reception between components is performed in real time using DDS(Data Distribution Service). Reliable Post-Analysis requires the collection of all DDS messages. However, Software workload and development costs increase because common functions for all messages must be developed directly. In the paper, We propose an improved Post-Analysis based on the Feature Model and a new development system using it. Functions for the modified message were applied as variable domains of the Feature Model. When Build Center updates Post-Analysis, it automatically processes all software tasks associated with Post-Analysis. The proposed development system simplified the overall work procedure, and work time was significantly reduced compared to the existing development system.

Naval CMS(Combat Management System) is linked to various sensors and weapon equipment and use DDS(Data Distribution Service) for efficient data communication between ICU(Interface Control Unit) Node and IPN(Information Processing Node). In order to use DDS, software in the system communicates in an PUB/SUB(Publication/Subscribe) based on DDS topic. If the DDS messages structure in this PUB/SUB method does not match, problems such as incorrect command processing and wrong information delivery occur in sending and receiving application software. To improve this, this paper proposes a DDS message structure integrity verification method. To improve this, this paper proposes a DDS message structure integrity verification method using a hash tree. To verify the applicability of the proposed method to Naval CMS, the message integrity verification rate of the proposed method was measured, and the integrity verification method was applied to CMS and the initialization time of the existing combat management system was compared and the hash tree generation time of the message structures was measured to understand the effect on the operation and development process of CMS. Through this test, It was confirmed that the message structure verification method for system stability proposed in this paper can be applied to the Naval CMS.

This study conducted K-POP cover dance for female marriage immigrants, and analyzed the effects of Self-esteem and social support on acculturation stress and depression to provide basic data for improving social relationships and promoting mental health of female marriage immigrants. Female marriage immigrants in their 20s and 30s were recruited, and 25 people each were randomly assigned to a high Self-esteem and social support group (HSS) and a low Self-esteem & social support group (LSS) according to the Self-esteem and social support of the pre-test, a total of 3 surveys were conducted, pre-test, after 6 weeks, after 12 weeks. The Self-esteem of female marriage immigrants following K-POP cover dance(60 minutes, 3 times a week, for 12 weeks) participation improved as the participation period in both HSS and LSS groups increased, and the LSS group improved significantly after 6 weeks. Social support decreased after 6 weeks in HSS and 12 weeks in LSS, The acculturation stress continued to decrease after 6 weeks and after 12 weeks in both HSS and LSS, and the LSS group decreased more significantly. Depression in both HSS and LSS groups decreased as the participation period increased, and the LSS group decreased significantly after 6 weeks. K-POP cover dance is thought to be an effective leisure activity program for female marriage immigrants to improve Self-esteem and social support, and to reduce acculturation stress and depression.

In this paper, among short-range wireless communication technologies such as Beacon, Bluetooth, UWB (Ultra-wideband), ZigBee, NFC (Near Field Communication), Z-Wave, 6LoWPAN (IPv6 over Low power WPAN), D2D (Device to Device), etc. , proposed an IoT service platform based on a beacon that can provide indoor positioning. And, a beacon-linked web server was designed by blocking indiscriminate beacon spam signals and applying REST web service technology with flexibility and scalability. Data accessibility between different devices was verified by testing the success rate of data transmission, the success rate of blocking beacon push, the success rate of IoT interlocking processing, the accuracy of location positioning, and the success rate of REST web service-based data processing. Through the designed IoT service platform, various proposals and research on short-distance-based business models and service platforms will be conducted in the future.

This study attempted to categorize the motivation for participation in leisure sports and analyze the difference in consumer demand for leisure sports wear according to the type. As a result of the analysis, the functionality of the preferred material consisted of movement, comfort, and body temperature maintenance. When purchasing, the factors to consider were extracted such as basic emphasis, emphasis on flaunting, and emphasis on practicality. The improvement requirement was composed of basic function improvement and various function additional factors, and the purchase satisfaction factor was composed of three factors: brand, practicality, design, and functionality. Motives for participation in marine leisure sports were grouped into mania, leisure, and health types. As for the functionality of the preferred material, the mania type and leisure type preferred movement, comfort, and body temperature maintenance functionality over the health type. As for the factors to consider when purchasing, the mania type and leisure type had higher consideration for the factors of basic emphasis, ostentation, and practicality than the health type. In the improvement requirements, both basic function improvement and various function additions had the highest mania type and the lowest health type. In terms of purchase satisfaction factors, practicality and brand were found to have a high leisure type, and functional factors were found to have a high mania type. For the enthusiastic group, it is thought that product development should focus on functionality and additional functions, and health types should be focused on design and utilization.

This study aims to provide basic data for reinforcing the learning competency of paramedic students by analyzing the performance, importance, and demand for the major curriculum of them. The participants of the study was 217 students from the Department of Emergency medical technology from 3 universities in Chungnam, and the survey data collection period was from December 13 to December 24, 2021. As a result of the study, 'Education for Ambulance management', 'Education for maintaining professionalism after graduation', 'Education for In-hospital patient monitoring' are highly required by Borich need, and 'Education for medical oder from a doctor, Education for han dover to In-hospital medical staff', 'Education for non-traumatic emergency patient treatment', 'Education for In-hospital patient monitoring', and 'Education for In-hospital medical assistance' are the top priority areas of the LF model. It is judged that it is necessary to reinforce the curriculum corresponding to in order to strengthen the learning capabilities of paramedic students.

In this paper, we propose a study is to prepare basic data for integrated health promotion education by preventing dry mouth and dry eyes through lifestyle management. From October 7 to 17, 2022, a total of 516 respondents who voluntarily agreed and responded through a self-written structured questionnaire were statistically analyzed for university students in their 20s across the country. As for the factor of feeling dry mouth among the living habits of the study subjects, the more smoking per day, the higher(8.41±2.041) and very high(7.75±2.927) felt dry mouth(p=.015), and the time spent using smart phones. The shorter this was, the lower(1.16±0.784) and very low(1.83±1.672) felt dry mouth(p=.022). The main factors contributing to dry mouth and dry eyes were dry eyes (odds ratio 3.651, p=.000), and high smoking(odds ratio 0.916, p=.038), and the more you use your smart phone (odds ratio 0.256, p=.004), the more you feel dry mouth. When feeling dry eyes, they felt dry mouth more(odds ratio 4.002, p=.000), and the more they exercised, the more dry eyes they felt(odds ratio 1.600, p=.009). As a result, since dry mouth and dry eyes appear as common inconveniences, it was found that lifestyle management is necessary to maintain and promote a healthy life. Therefore, it is proposed to build an integrated health platform that can improve the quality of life and implement personalized health management programs.

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

In this paper, we proposes a method of optimizing the processing order of skids to minimize the span time in a conveyor environment of the shipbuilding subassembly process. The subassembly process consists of a series of fixed tasks where the required work time is varied according to the skid type. The loading order of skids on a conveyor which determines the span time should be properly optimized and the problem size exponentially increases with the number of skids. In this regard, we propose a novel method called UniDev by defining a measure of the mean-absolute-deviation about the time difference among simultaneously processed tasks and iteratively improving it. Through simulations with various numbers of skids and processes, it was observed that our proposed method can efficiently reduce the overall work time compared with the multi-start and the 2-OPT methods.

This study examined the effects of professionalism and critical thinking disposition on job competency in nursing service workers. Furthermore, we explored the interactive impact of critical thinking disposition in terms of component factors in the relationship between the influence of professionalism on job performance. Our research results were as follows. First, we found that the self-concept of the profession and originality of the job in the professional view has a significant effect on job performance. Second, we discovered that systematicity and objectivity in the critical thinking disposition significantly affect job performance. Third, we found an interaction effect between each component of professionalism and critical thinking disposition. This study is significant because it is approached from the perspective of components of professionalism and the critical thinking disposition to improve job performance. In addition, the results of this study will contribute to the management of professionalism and essential dispositions of thinking to enhance nursing service workers' job performance.

Based on the VAM model of the consumer behavior research framework, combined with the altruistic variable of environmental awareness, this paper constructs a research model on the impact of perceived value, perceived risk and environmental awareness on purchase intention, and deeply analyzes the driving factors of perceived value and perceived risk. By collecting 612 valid questionnaires, data was analyzed by using SPSS 24.0 and AMOS 24.0, the results were concluded. Perceived usefulness, perceived entertainment, Technicality, and Energy Awareness all have a significant positive impact on the purchase intention of new energy vehicles; Perceived Fee has a negative impact on purchase intention. The results of this study can provide strategies for companies to improve marketing, reduce consumers' perceived risks, provide beneficial supplements to companies in promoting the environmental protection attributes of new energy vehicles and their own environmental protection efforts, and promote the balance of economic and environmental benefits.