Search

Abstract Target detection in autonomous driving tasks presents a complex and critical challenge due to the diversity of targets and the intricacy of the environment. To address this issue, this paper proposes an enhanced YOLOv8 model. Firstly, the original large target detection head is removed and replaced with a detection head tailored for small targets and high‐level semantic details. Secondly, an adaptive feature fusion method is proposed, where input feature maps are processed using dilated convolutions with different dilation rates, followed by adaptive feature fusion to generate adaptive weights. Finally, an improved attention mechanism is incorporated to enhance the model's focus on target regions. Additionally, the impact of Group Shuffle Convolution (GSConv) on the model's detection speed is investigated. Validated on two public datasets, the model achieves a mean Average Precision (mAP) of 53.7% and 53.5%. Although introducing GSConv results in a slight decrease in mAP, it significantly improves frames per second. These findings underscore the effectiveness of the proposed model in autonomous driving tasks.

Ceramic slurry is the raw material used in stereolithography, and its performance determines the printing quality. Rheological behavior, one of the most important physical factors in stereolithography, is critical in ceramic printing, significantly affecting the flow, spreading, and printing processes. The rheological behavior of SiO2 slurry used in stereolithography technology is investigated in the current research using different powder diameters and temperatures. The results present the apparent non-Newtonian behavior. The yielding characteristics occur in all cases. For single-powder cases, the viscosity decreases when the powder diameter is increased. When the nano-sized and micro-sized powders are mixed in different proportions, a more significant proportion of micron-sized powders will decrease the viscosity. With an increase in the nano-sized powders, the slurry exhibits the shear thinning behavior; otherwise, the shear thickening behavior is observed. Thus, the prediction model is built based on the use of the pelican optimization algorithm-deep extreme learning machine (POA-DELM), and the model in then compared with the fitted and traditional models to validate the effectiveness of the method. A more accurate viscosity prediction model will contribute to better fluid dynamic simulation in future work.

A novel type of titanium matrix composite (TMC) with a uniform network microstructure has been successfully fabricated by adjusting particle-size mismatch (Φ). This study can also improve the understanding of the effects of particle size on microstructure and mechanical properties, particularly in titanium matrix composites reinforced with graphite flakes (GNFs). Microstructural analysis reveals the absence of noticeable defects, and significant grain refinements have been realized. The experimental results indicate that the yield strength of the mismatched composite is improved by 24.75% compared to that of normal composites. The micro-hardness also exhibits a 10.3% increase. These enhancements can be attributed to the introduction of particle-size mismatch, the refinement of the microstructure, and the deflection of interface cracks. The presence of distorted GNF lattices in the interface micro-region of the composites primarily results from the appropriate sizing of different particles.

The essence of PHM technology is to process the collected information with the help of the system information collected by sensors, using information fusion, artificial intelligence, big data, reasoning algorithms and other technologies, and realize the monitoring management, status evaluation and fault prediction functions of the target system. PHM is an important part of the intelligent equipment detection and maintenance system. Its application and realization in the railway field is the key link of the intelligent operation and maintenance of multiple units, and is an important means to realize the shift from planned preventive maintenance to digital and accurate condition maintenance. It is of great significance for China's high-speed railway to maintain the world's advanced level and move towards higher quality, efficiency and efficiency. With the improvement of operation speed and the growth of application scale of High-Speed Electric Multiple Units in China, hereinafter referred to as EMU, the technical challenges of operation safety and security of EMUs are increasingly prominent. As a kind of equipment health management technology, PHM can realize equipment status monitoring, abnormal prediction, fault diagnosis, maintenance prediction and maintenance decision-making. In order to improve the safety assurance capability of high-speed EMU, reduce the maintenance cost and improve the maintenance efficiency, this paper deeply integrates big data technology, algorithm model and PHM technology, and explores the theory and method of intelligent fault prediction of key components of high-speed EMU based on PHM technology. Focus on the research of EMU condition monitoring and fault diagnosis technology based on HSMM and DBN algorithms, as well as the component maintenance prediction and maintenance decision-making technology based on fixed repair schedule prevention, so as to transfer the theoretical basis and technical support for the maintenance mode of EMU from "planned repair" to "planned repair predictive maintenance". [ABSTRACT FROM AUTHOR]

Innovative exploration of transmission and dissemination of intangible cultural heritage plays a key role in national development, international exchange, and sustainable social development. This review presents the current state of digital preservation in China in the field of intangible cultural heritage based on contemporary approaches via study and development of interactive digital resources such as shadow puppets. Using design-based research, quasi-experimental methods, and literature review, this study provides an understanding of the current status of the development and preservation of intangible cultural heritage. Three different versions of shadow puppet interactive digital resources are presented using storyline software. Subjects learned best when viewing the global-no-catalog type of interactive resources, moderately well when viewing the left-right layout type, and the least when viewing the top-down layout. [ABSTRACT FROM AUTHOR]

As interest in the development of artificial intelligence technology in the water supply field increases, an artificial neural network algorithm that can predict improved decision ratings through repetitive learning using results of aging pipe condition evaluation data should be developed and the most reliable prediction model should be presented through a verification process. An algorithm was developed to predict pipeline ratings by updating weights through backpropagation so that 12 items of indirect evaluation data according to the 2020 Han River Basin's basic plan could be pre-processed, such as standardizing input values and applying artificial neural network algorithms. As a result of algorithm accuracy verification, if there is sufficient data and the repetitive learning and upgrades are continuously conducted, the prediction accuracy will become higher and a reliable AI-based water supply pipe condition evaluation model to be used nationwide will be developed in the future. [ABSTRACT FROM AUTHOR]

Among various fields at home and abroad, machine learning technology is developing rapidly, centering on deep learning. Active research is being conducted. However, due to the lack of sufficient machine learning data, opportunities such as providing high value-added content and improving service performance are missed. New entity names are registered in various documents and interpreted as different entity names depending on the context. Through prescriptive analysis-based key entity name recognition technology that collects researcher's research field, research propensity, and research activity information and converts the value of data into new phrase-unit information into word-unit qualities by constructing and utilizing general-purpose data, we provide a research information curation service based on prescriptive analysis to solve difficulties of securing learning data for researchers who want to apply machine learning and for obtaining customized research information that corresponds to individualized services for each researcher. [ABSTRACT FROM AUTHOR]

Laser oscillation possesses a stimulating impact on the melt pool, causing the disruption of columnar crystals and enhancing the creation of newly nucleated masses. As a consequence, an enhanced microstructure and a more refined grain size are achieved. The study compares the microstructure, phase composition, and mechanical properties of as-deposited and as-annealed TC4 alloys via various oscillation paths. The results show that the microstructure of the deposited TC4 alloys under the four oscillation paths is primarily acicular and cluster martensitic. Additionally, the plasticity of the specimens in the annealed state displays an overall improvement, except for linear oscillation, which experiences a decrease in overall yield strength and ultimate tensile strength. This can be attributed to the sub-stable α’ phase undergoing coarsening during annealing and eventually decomposing into (α + β) phase tissue. The β phase, with a BCC structure and more slip systems, has a lower tensile strength but greater plasticity. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants. [ABSTRACT FROM AUTHOR]

To improve the cooperation ability of emergency entities, in this research, the emergency activities model, skill contribution degree, entity-relationship strength, activity continuity, and emergency entity cooperation degree were defined. Emergency entity cooperation degree and emergency activity continuity table were constructed with emergency cases, emergency plans, and emergency drill plans, and factors were further excavated which affected them. In this paper, we focus on the factors which affect the cooperation ability of emergency entities, the relationship between the emergency cooperation ability and the number of cooperation, entity-relationship intensity, emergency activity frequency, and skill contribution of fire entities and medical entities were obtained. These data results are of great significance to decision-makers in formulating emergency rescue and emergency plans. [ABSTRACT FROM AUTHOR]

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold crossvalidation, and the precision was 89.5%, which can provide the certain basis for government decision-making. [ABSTRACT FROM AUTHOR]

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time. [ABSTRACT FROM AUTHOR]

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment. [ABSTRACT FROM AUTHOR]

For a library to be able provide information services and fulfill its function as a knowledge convergence center capable of responding to various information demands, the development of next-generation information systems based on the latest information and communication technology is needed. The development of mobile information services using portable devices such smart phones and tablet PCs and information systems which incorporate the concepts of cloud computing, SaaS (Software as a Service), annotation and Library2.0 is also required. This paper describes a library information system that utilizes collective intelligence and cloud computing. The information system developed for this study adopts the SaaS-based cloud computing service concept to cope with the shift in the mobile service paradigm in libraries and the explosion of electronic data. The strengths of such a conceptual model include the sharing of resources, support of multi-tenants, and the configuration and support of metadata. The user services are provided in the form of software on-demand. To test the performance of the developed system, the efficiency analysis and TTA certification test were conducted. The results of performance tests, It is encouraging that, at least up to 100MB, the job time is approximately linear and with only a moderate overhead of less than one second. The system also passed the level-3 or higher criteria in the certification test, which includes the SaaS maturity, performance and application program functions. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to design and implement mobile u-health system by defining the essential elements of mobile healthcare services. We choose common service elements for the proposed u-healthcare scheme and design the service platform. Especially we focus on automatic feces or urine sensing u-care scheme to prove the effectiveness of our platform. We construct the system with sensing part with a manikin and a diaper, wireless communication part with feces or urine sensing data, and coordinator system based on the u-health platform defined in this paper. Experimental results show that our scheme is useful in the area of u-care service for the handicapped, the elderly, and patients who can hardly move by themselves. In addition the designed scheme offers a realized u-care scheme with the purpose of advanced developing tools for application or service developers. [ABSTRACT FROM AUTHOR]

A traditional village is cultural property where old buildings and traditions are concentrated and also where indigenous traditional cultures can be recognized and experienced directly or indirectly. In addition, traditional villages have been recognized as precious resources to revitalize regional economies through tourism. Currently, signage systems have been developed as for ordinary tourist sites or cities, and concepts or methods for sign system for traditional villages have not yet been established. Therefore, it is necessary to develop disaster prevention-based signage to prevent large-scale disasters, to guarantee the safety of residents, and to increase the satisfaction of visitors in traditional villages as precious resources. A case study is conducted to apply the proposed sign system for traditional villages in Korea and Japan as a tourist-oriented sign system that has been accomplished in Korea and a large disaster-oriented sign system developed with residents as a priority in Japan. The disaster prevention sign system considers the residents since residents are victims when a disaster happens, and guideline to increase the efficacy and satisfaction are suggested. However, this study has definite limits in sucring objectivity because the objects of study are insufficient, and the development of the sign system based on effective disaster prevention considering the features of traditional villages will continue in the future. [ABSTRACT FROM AUTHOR]

This paper describes design and development of a system that supports continuous creation of hazard maps by local residents in their daily life. We made an interview survey to design our system in a model traditional town in Saga, Japan. The results show that in spite of continuous efforts, many practical problems remain and residents feel unsafe. Based on these results, we designed and developed a unique ICT-based support system which contributes to community-based disaster prevention/reduction. The continuous resident participation and posting design are core concept for our sustainable community-based approach. Our system continues to support making a hazard map by integrating the community-based hazard information. Local residents register information about the spot (disaster types, a risk level, a photograph, comments, positional information) that can be dangerous in case of disaster. We have evaluated the usefulness and possibilities of our prototype system implemented as an iOS application. [ABSTRACT FROM AUTHOR]

Relative entropy is a divergence measure between two probability density functions of a random variable. Assuming that the random variable has only two alphabets, the relative entropy becomes a cross-entropy error function that can accelerate training convergence of multi-layer perceptron neural networks. Also, the n-th order extension of cross-entropy (nCE) error function exhibits an improved performance in viewpoints of learning convergence and generalization capability. In this paper, we derive a new divergence measure between two probability density functions from the nCE error function. And the new divergence measure is compared with the relative entropy through the use of three-dimensional plots. [ABSTRACT FROM AUTHOR]

Sensors, equipment, ICT facilities and their corresponding software have a relatively short lifetime relative to that of constructional structure, so these devices have to be continuously fixed or exchanged during maintenance and management. Furthermore, software or analysis tools should be periodically upgraded according to advances in ICT and analysis technology. Conventional monitoring systems have serious problems in that it is difficult for site engineers to modify or upgrade hardware and analysis algorithms. Moreover, we depend on the original system developer when we want to modify or upgrade inner program structures. In this paper, we propose a novel design for integrated maintenance and management of a monitoring system by applying the mobile cloud concept. The system is intended for use in disaster prevention of constructional structures, including bridges, tunnels, and in traditional buildings in a local heritage village, we analyze the status of these structures over a long term or a short-term period as well as in disaster situations. Data are collected over a mobile cloud and future expectations are analyzed according to probabilistic and statistical techniques. We implement our integrated monitoring system to solve the existing problems mentioned above. The final goal of this study is to design and implement a monitoring system for more than 10,000 structures spread within Korea. Furthermore, we can specifically apply the monitoring system presented here to a bridge made from timber in Asan Oeam Village and a traditional house in Andong Hahoe Village to monitor for possible disasters. The entire system design and implementation can be developed on the LinkSaaS platform and the monitoring services can also be implemented on the platform. We prove that the proposed system has good performance by performing a TTA authentication test, web accommodation test, and operation test using emulated data. [ABSTRACT FROM AUTHOR]

Recent years have seen a significant increase in demand for multimedia data over wireless sensor networks for monitoring applications that utilize sensor nodes to collect multimedia data, including sound and video. However, the multimedia streams generate a very large amount of data. When data transmission schemes for traditional wireless sensor networks are applied in wireless multimedia sensor networks, the network lifetime significantly decreases due to the excessive energy consumption of specific nodes. In this paper, we propose a data compression scheme that implements the Chinese remainder theorem to a wireless multimedia sensor network. The proposed scheme uses the Chinese Remainder Theorem (CRT) to compress and split multimedia data, and it then transmits the bit-pattern packets of the remainder to the base station. As a result, the amount of multimedia data that is transmitted is reduced. The superiority of our proposed scheme is demonstrated by comparing its performance to that of an existing scheme. The results of our experiment indicate that our proposed scheme significantly increased the compression ratio and reduced the compression operation in comparison to those of existing compression schemes. [ABSTRACT FROM AUTHOR]

Many severe earthquakes have occurred along the main fault lines in the world. In recent years, there has been a high incidence of small- and large-scale earthquakes in our country. These occurrences of earthquakes have become increasingly serious, owing to a degrading earth environment. An earthquake, in general, causes far more serious damages like disruptions in electricity or gas facilities, and fire disasters from its annexed effects than by its vibrations or the shock itself. In this paper, we present a countermeasure and its necessity in safeguarding against fire disasters in regional heritage villages such as "Asan Oeam," "Jeonju Hanok," and "Andong Hahoe." A more systematic and effective strategy for prevention systems against severe fire disasters is proposed after performing various related investigations and analyses of existing domestic and international systems. We investigated the existing fire security systems and their historical records with the tendency of earthquake occurrences in these three folk villages. In this proposal, we present a strategic approach for safeguarding against fire damages in our regional heritage villages derived from ICT (information communication technology)-based DPD (disaster prevention design), after examining the laws and regulations of fire-prevention strategies in Japan, America, and Europe. [ABSTRACT FROM AUTHOR]

Management systems for electronic library have been developed on the basis of Client/Server or ASP framework in domestic market for a long time. Therefore, both service provider and user suffer from their high cost and effort in management, maintenance, and repairing of software as well as hardware. Recently in addition, mobile devices like smartphone and tablet PC are frequently used as terminal devices to access computers through the Internet or other networks, sophisticatedly customized or personalized interface for n-screen service became more important issue these days. In this paper, we propose a new scheme of integrated management system for electronic library based on SaaS and Web Standard. We design and implement the proposed scheme applying Electronic Cabinet Guidelines for Web Standard and Universal Code System. Hosted application management style and software on demand style service models based on SaaS are basically applied to develop the management system. Moreover, a newly improved concept of duplication check algorithm in a hierarchical evaluation process is presented and a personalized interface based on web standard is applied to implement the system. Algorithms of duplication check for journal, volume/number, and paper are hierarchically presented with their logic flows. Total framework of our development obeys the standard feature of Electronic Cabinet Guidelines offered by Korea government so that we can accomplish standard of application software, quality improvement of total software, and reusability extension. Scope of our development includes core services of library automation system such as acquisition, list-up, loan-and-return, and their related services. We focus on interoperation compatibility between elementary sub-systems throughout complex network and structural features. Reanalyzing and standardizing each part of the system under the concept on the cloud of service, we construct an integrated development environment for generating, test, operation, and maintenance. Finally, performance analyses are performed about resource usability of server, memory amount used, and response time of server etc. As a result of measurements fulfilled over 5 times at different test points and using different data, the average response time is about 62.9 seconds for 100 clients, which takes about 0.629 seconds per client on the average. We can expect this result makes it possible to operate the system in real-time level proof. Resource usability and memory occupation are also good and moderate comparing to the conventional systems. As total verification tests, we present a simple proof to obey Electronic Cabinet Guidelines and a record of TTA authentication test for topics about SaaS maturity, performance, and application program features. [ABSTRACT FROM AUTHOR]