• Industry Promotion Research
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• v.9 no.3
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• pp.295-309
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• 2024

With the implementation of the Northeast Revitalization Strategy, the tourism industry in Heilongjiang Province had an increasingly greater impact on regional economic development. Based on the tourism panel data of Heilongjiang Province from 2005 to 2021, this paper used DEA-BCC and Malmquist Index to analyze the static and dynamic changes of the tourism industry.The results of the study were as follows: (1) Static: The OE value reached strong DEA effectiveness in 2010, 2013, and 2019, indicated that tourism resources had been fully utilized. The SE value changed dramatically between 0.354 and 1, and the PTE value approached 1. OE was mainly affected by SE changes. (2) Dynamic: The total factor productivity (TFP) was overall greater than 1 and grew at an average annual rate of 13.8%. The variation in TFP was primarily influenced by the index of technological progress, indicated that the tourism industry in Heilongjiang Province made full use of technology for resource development, with a relatively high level of development efficiency. Therefore, the future focus of Heilongjiang Province's tourism industry will be on adjustments in industrial scale, technological innovation, and policy optimization.

• Applied Chemistry for Engineering
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• v.35 no.4
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• pp.316-320
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• 2024

To optimize the emulsion stability and antibacterial activity against Escherichia coli (E. coli) of cosmeceuticals using Scutellaria baicalensis extracts and olive wax as natural emulsifiers, we conducted a study. The independent variables were the amounts of Scutellaria baicalensis extracts and olive wax added. The response variables included the emulsion stability index (ESI) of the cosmeceuticals product and the inhibition diameter against E. coli. Through central composite design-response surface methodology (CCD-RSM), we obtained a statistically significant and reliable regression equation within a 95% confidence interval. By optimizing multiple responses, we determined that the optimal emulsification conditions that satisfied both ESI and E. coli inhibition diameter were 3.7 wt% of Scutellaria baicalensis extracts and 2.7 wt% of olive wax. The predicted ESI and E. coli inhibition diameter were 97.9% and 9.7 mm, respectively. When actual experiments were conducted under the optimal conditions, the measured ESI and E. coli inhibition diameter were 95.0% and 9.4 mm, respectively, with an average error rate of 3.2 ± 0.4%.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.4
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• pp.36-47
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• 2009

Microbial and related biochemical mass of composts are important for optimization of its process and end-products. This study was carried out to assess the specific microbial and related biochemical mass which could be used as an indicator for compost maturity during composting stages. The samples from five compost plants were collected at three stages (Initial, Thermophilic and Mature) and analyzed for total aerobic bacteria (TAB), Coliforms, Escherichia coli, Actinomycetes and fungi. Significantly, the coliforms and E.coli counts decreased during the thermophilic stage and were completely eliminated during mature stage. However, the other microbial mass were completely eliminated during mature stage. Which disclosed that Coliforms and E.coli communities can be used as compost maturity indicator. Interestingly, the microbial biomass carbon and nitrogen ratio (MBC/MBN) were decreased a little during the thermophilic stage due to the decreasing number of coliforms, Ecoli and fungi, while the ratio increased during the mature stage due to increasing fungal and aerobic bacterial counts. In addition the heavy metals were shown strong negative correlation with Actenomycetes. This study provides insight to the evaluation of compost maturity as well as the quality by the metal-microbial interactions.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.135-140
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• 2024

It is said that PV power generation systems have a long lifespan compared to other renewable energy sources and require little maintenance. However, there are cases where the performance expected during initial design is not achieved due to shading, temperature rise, mismatch, contamination/deterioration of PV modules, failure of inverter, leakage current, and arc generation. Therefore, in order to solve the problems of these systems, the power generation amount and operation status are investigated qualitatively, or the performance is comparatively analyzed based on the performance ratio (PR), which is the performance index of the solar power generation system. However, because it includes large losses, it is difficult to accurately determine whether there are any abnormalities such as performance degradation, failure, or defects in the PV power generation system using only the performance coefficient. In this paper, we studied a status diagnosis algorithm for shading, inverter failure, leakage, and arcing of PV modules to optimize the power generation of PV power generation systems according to changes in the surrounding environment. In addition, using the studied algorithm, we examined the results of an empirical test on condition diagnosis for each area and the resulting optimized operation of power generation.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Progress in Medical Physics
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• v.25 no.2
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• pp.79-88
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• 2014

The dose distributions within the real volumes of tumor targets and critical organs during internal target volume-based intensity-modulated radiation therapy (ITV-IMRT) for liver cancer were recalculated by applying the effects of actual respiratory organ motion, and the dosimetric features were analyzed through comparison with gating IMRT (Gate-IMRT) plan results. The ITV was created using MIM software, and a moving phantom was used to simulate respiratory motion. The doses were recalculated with a 3 dose-volume histogram (3DVH) program based on the per-field data measured with a MapCHECK2 2-dimensional diode detector array. Although a sufficient prescription dose covered the PTV during ITV-IMRT delivery, the dose homogeneity in the PTV was inferior to that with the Gate-IMRT plan. We confirmed that there were higher doses to the organs-at-risk (OARs) with ITV-IMRT, as expected when using an enlarged field, but the increased dose to the spinal cord was not significant and the increased doses to the liver and kidney could be considered as minor when the reinforced constraints were applied during IMRT plan optimization. Because the Gate-IMRT method also has disadvantages such as unsuspected dosimetric variations when applying the gating system and an increased treatment time, it is better to perform a prior analysis of the patient's respiratory condition and the importance and fulfillment of the IMRT plan dose constraints in order to select an optimal IMRT method with which to correct the respiratory organ motional effect.

• Journal of radiological science and technology
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• v.31 no.4
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• pp.401-406
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• 2008

The aim of this study is to evaluate contra-lateral breast (CLB) surface dose in Field-in-Field (FIF) technique for breast conserving surgery patients. For evaluation of surface dose in FIF technique, we have compared with other techniques, which were open fields (Open), metal wedge (MW), and enhanced dynamic wedge (EDW) techniques under same geometrical condition and prescribed dose. The three dimensional treatment planning system was used for dose optimization. For the verification of dose calculation, measurements using MOSFET detectors with Anderson Rando phantom were performed. The measured points for four different techniques were at the depth of 0cm (epidermis) and 0.5cm bolus (dermis), and spacing toward 2cm, 4cm, 6cm, 8cm, 10cm apart from the edge of tangential medial beam. The dose calculations were done in 0.25cm grid resolution by modified Batho method for inhomogeneity correction. In the planning results, the surface doses were differentiated in the range of $19.6{\sim}36.9%$, $33.2{\sim}138.2%$ for MW, $1.0{\sim}7.9%$, $1.6{\sim}37.4%$ for EDW, and for FIF at the depth of epidermis and dermis as compared to Open respectively. In the measurements, the surface doses were differentiated in the range of $11.1{\sim}71%$, $22.9{\sim}161%$ for MW, $4.1{\sim}15.5%$, $8.2{\sim}37.9%$ for EDW, and 4.9% for FIF at the depth of epidermis and dermis as compared to Open respectively. The surface doses were considered as underestimating in the planning calculation as compared to the measurement with MOSFET detectors. Was concluded as the lowest one among the techniques, even if it was compared with Open method. Our conclusion could be stated that the FIF technique could make the optimum dose distribution in Breast target, while effectively reduce the probability of secondary carcinogenesis due to undesirable scattered radiation to contra-lateral breast.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.337-347
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• 2007

An embedded system is called a multi-mode embedded system if it performs multiple applications by dynamically reconfiguring the system functionality. Further, the embedded system is called a multi-mode multi-task embedded system if it additionally supports multiple tasks to be executed in a mode. In this Paper, we address a HW/SW partitioning problem, that is, HW/SW partitioning of multi-mode multi-task embedded applications with timing constraints of tasks. The objective of the optimization problem is to find a minimal total system cost of allocation/mapping of processing resources to functional modules in tasks together with a schedule that satisfies the timing constraints. The key success of solving the problem is closely related to the degree of the amount of utilization of the potential parallelism among the executions of modules. However, due to an inherently excessively large search space of the parallelism, and to make the task of schedulabilty analysis easy, the prior HW/SW partitioning methods have not been able to fully exploit the potential parallel execution of modules. To overcome the limitation, we propose a set of comprehensive HW/SW partitioning techniques which solve the three subproblems of the partitioning problem simultaneously: (1) allocation of processing resources, (2) mapping the processing resources to the modules in tasks, and (3) determining an execution schedule of modules. Specifically, based on a precise measurement on the parallel execution and schedulability of modules, we develop a stepwise refinement partitioning technique for single-mode multi-task applications. The proposed techniques is then extended to solve the HW/SW partitioning problem of multi-mode multi-task applications. From experiments with a set of real-life applications, it is shown that the proposed techniques are able to reduce the implementation cost by 19.0% and 17.0% for single- and multi-mode multi-task applications over that by the conventional method, respectively.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.67-75
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• 2012

Purpose : We studied enhanced method to view the vessels in the brain using Magnetic Resonance Angiography (MRA). Noticing that Maximum Intensity Projection (MIP) image is often used to evaluate the arteries of the neck and brain, we propose a new method for view brain vessels to stereo image in 3D space with more superior and more correct compared with conventional method. Materials and Methods: We use 3T Siemens Tim Trio MRI scanner with 4 channel head coil and get a 3D MRA brain data by fixing volunteers head and radiating Phase Contrast pulse sequence. MRA brain data is 3D rotated according to the view angle of each eyes. Optimal view angle (projection angle) is determined by the distance between eye and center of the data. Newly acquired MRA data are projected along with the projection line and display only the highest values. Each left and right view MIP image is integrated through anaglyph imaging method and optimal stereoscopic MIP image is acquired. Results: Result image shows that proposed method let enable to view MIP image at any direction of MRA data that is impossible to the conventional method. Moreover, considering disparity and distance from viewer to center of MRA data at spherical coordinates, we can get more realistic stereo image. In conclusion, we can get optimal stereoscopic images according to the position that viewers want to see and distance between viewer and MRA data. Conclusion: Proposed method overcome problems of conventional method that shows only specific projected image (z-axis projection) and give optimal depth information by converting mono MIP image to stereoscopic image considering viewers position. And can display any view of MRA data at spherical coordinates. If the optimization algorithm and parallel processing is applied, it may give useful medical information for diagnosis and treatment planning in real-time.