• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.361-368
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• 2002

The aim of this study was to investigate the effect of preprocessing the transmitted energy spectrum data on development of a robust model to predict the sugar content in intact apples. The spectrum data were measured from 120 Fuji apple samples conveying at the speed of 2 apples per second. Computer algorithms of preprocessing methods such as MSC, SNV, first derivative, OSC and their combinations were developed and applied to the raw spectrum data set. The results indicated that correlation coefficients between the transmitted energy values at each wavelength and sugar contents of apples were significantly improved by the preprocessing of MSC and SNV in particular as compared with those of no-preprocessing. SEPs of the prediction models showed great difference depending on the preprocessing method of the raw spectrum data, the largest of 1.265%brix and the smallest of 0.507% brix. Such a result means that an appropriate preprocessing method corresponding to the characteristics of the spectrum data set should be found or developed for minimizing the prediction errors. It was observed that MSC and SNV are closely related to prediction accuracy, OSC is to number of PLS factors and the first derivative resulted in decrease of the prediction accuracy. A robust calibration model could be d3eveloped by the combined preprocessing of MSC and OSC, which showed that SEP=0.507%brix, bias=0.0327 and R2=0.8823.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1062-1062
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• 2001

The concept of “precision agriculture” or “site-specific farming” is usually confined to the fields of soil science, crop science and agronomy. However, because plants grow in soil, animals eat plants, and humans eat animal products, it could be argued (perhaps with some poetic licence) that the fields of feed quality, animal nutrition and animal production should also be considered in this context. NIR spectroscopy has proved over the last 20 years that it can provide a firm foundation for quality measurement across all of these fields, and with the continuing developments in instrumentation, computer capacity and software, is now a major cog in the wheel of precision agriculture. There have been a few giant leaps and a lot of small steps in the impact of NIR on the animal world. These have not been confined to the amazing advances in hardware and software, although would not have occurred without them. Rapid testing of forages, grains and mixed feeds by NIR for nutritional value to livestock is now commonplace in commercial laboratories world-wide. This would never have been possible without the pioneering work done by the USDA NIR Forage Research Network in the 1980's, following the landmark paper of Norris et al. in 1976. The advent of calibration transfer between instruments, algorithms which utilize huge databases for calibration and prediction, and the ability to directly scan whole grains and fresh forages can also be considered as major steps, if not leaps. More adventurous NIR applications have emerged in animal nutrition, with emphasis on estimating the functional properties of feeds, such as in vivo digestibility, voluntary intake, protein degradability and in vitro assays to simulate starch digestion. The potential to monitor the diets of grazing animals by using faecal NIR spectra is also now being realized. NIR measurements on animal carcasses and even live animals have also been attempted, with varying degrees of success, The use of discriminant analysis in these fields is proving a useful tool. The latest giant leap is likely to be the advent of relatively low-cost, portable and ultra-fast diode array NIR instruments, which can be used “on-site” and also be fitted to forage or grain harvesters. The fodder and livestock industries are no longer satisfied with what we once thought was revolutionary: a 2-3 day laboratory turnaround for fred quality testing. This means that the instrument needs to be taken to the samples rather than vice versa. Considerable research is underway in this area, but the challenge of calibration transfer and maintenance of instrument networks of this type remains. The animal world is currently facing its biggest challenges ever; animal welfare, alleged effects of animal products on human health, environmental and economic issues are difficult enough, but the current calamities of BSE and foot and mouth disease are “the last straw” NIR will not of course solve all these problems, but is already proving useful in some of these areas and will continue to do so.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1573-1582
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• 2011

Risk of lung cancer among lung-related diseases has gradually increased during last decades. The chest digital radiography is the primary diagnosis method for lung cancer. Diagnosing lung cancer using this method requires doctors of ripe experience. Despite their experience there are often wrong diagnoses, which decrease early diagnosis and survival rates of patients. The aim of this study was intended to establish the base on the Computer Aided Diagnosis (CAD) by analyzing Image Recognition Algorithm using Principle component Analysis (PCA) and diagnosing patient's chest X-ray image. The database obtained through this approach enables a doctor to significantly reduce misdiagnosis during the early diagnosis stage, if he or she utilizes it as the preliminary reading step. Case studies were carried out using normal organ, and organs suffering from bronchogenic carcinoma and granuloma. A normal image and unique disease images were extracted after PCA analysis, and their cross-recognition efficiency were compared each other. The result revealed that the recognition rate was much high between normal and disease images, but relatively low between two disease images. In order to increase the recognition efficiency among chest diseases the related algorithms have to be developed continuously in the future study, and such effort will establish the resolute base for CAD.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.571-580
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• 2005

In fast long-distance networks, TCP's congestion control algorithm has the problem of utilizing bandwidth effectively. Several window-based congestion control protocols for high-speed and large delay networks have been proposed to solve this problem. These protocols deliberate mainly three properties : scalability, TCP-friendliness, and RTT-fairness. These protocols, however, cannot satisfy above three properties at the same time because of the trade-off among them This paper presents a new window-based congestion control algorithm, called EM (Exponential Increase/ Multiplicative Decrease), that simultaneously supports all four properties including fast convergence, which is another important constraint for fast long-distance networks; it can support scalability by increasing congestion window exponentially proportional to the time elapsed since a packet loss; it can support RTT-fairness and TCP-friendliness by considering RTT in its response function; it can support last fair-share convergence by increasing congestion window inversely proportional to the congestion window just before packet loss. We evaluate the performance of EIMD and other algorithms by extensive computer simulations.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.573-586
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• 2008

The improved performance and miniaturization of computer and the improvement of wireless communication technology have enabled the emergence of many high quality services. Among them multicast services are receiving much attention and their usage is increasing due to the increase of Internet multimedia services such as video conference, multimedia stream, internet TV, etc. Security plays an important role in mobile multicast services. In this paper, we proposed a secure multicast protocol for a hierarchical micro-mobility environment. The proposed secure multicast protocol provides security services such as authentication, access control, confidentiality and integrity using mechanisms including symmetric/asymmetric key crypto-algorithms and capabilities. To provide forward/backward secrecy and scalability, we used sub-group keys based on the hierarchical micro-mobility environment. With this security services, it is possible to guard against all kinds of security attacks performed by illegal mobile nodes. Attacks executed by internal nodes can be thwarted except those attacks which delete packet or cause network resources to be wasted. We used simulator to measure the performance of proposed protocol. As a result, the simulation showed that effect of these security mechanisms on the multicast protocol was not too high.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Therapeutic Science for Rehabilitation
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• v.7 no.1
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• pp.79-88
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• 2018

Objective : Conventional therapy approaches for stroke survivors have required considerable demands on therapist's effort and patient's expense. Thus, new robotics rehabilitation therapy technologies have been proposed but they have suffered from less than optimal control algorithms. This article presents a novel technical healthcare solution for the real-time, simultaneous and propositional myoelectric control for stroke survivors' upper limb robotic rehabilitation therapy. Methods : To implement an appropriate computational algorithm for controlling a portable rehabilitative robot, a linear regression model was employed, and a simple game experiment was conducted to identify its potential of clinical utilization. Results : The results suggest that the proposed device and computational algorithm can be used for stroke robot rehabilitation. Conclusion : Moreover, we believe that these techniques will be used as a prominent tool in making a device or finding new therapy approaches in robot-assisted rehabilitation for stroke survivors.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.135-146
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• 2004

For improving the effectiveness of travel information, some rational paths are needed to provide them to users driving in real road network. To meet it, k-shortest path algorithms have been used in general. Although the k-shortest path algorithm can provide several alternative paths, it has inherent limit of heavy overlapping among derived paths, which nay lead to incorrect travel information to the users. In case of considering the network consisting of several turn prohibitions popularly adopted in real world network, it makes difficult for the traditional network optimization technique to deal with. Banned and penalized turns are not described appropriately for in the standard node/link method of network definition with intersections represented by nodes only. Such problem could be solved by expansion technique adding extra links and nodes to the network for describing turn penalties, but this method could not apply to large networks as well as dynamic case due to its overwhelming additional works. This paper proposes a link-based shortest path algorithm for the travel information in real road network where exists turn prohibitions. It enables to provide efficient alternative paths under consideration of overlaps among paths. The algorithm builds each path based on the degree of overlapping between each path and stops building new path when the degree of overlapping ratio exceeds its criterion. Because proposed algorithm builds the shortest path based on the link-end cost instead or node cost and constructs path between origin and destination by link connection, the network expansion does not require. Thus it is possible to save the time or network modification and of computer running. Some numerical examples are used for test of the model proposed in the paper.

• Radiation Oncology Journal
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• v.12 no.2
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• pp.243-252
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• 1994

The use of high dose rate remote afterloading system for the treatment of intraluminal lesions necessitates the need for a more accurate of dose distributions around the high intensity brachytherapy sources, doses are often prescribed to a distance of few centimeters from the linear source, and in this range the dose distribution is very difficult to assess. Accurated and optimized dose calculation with stable numerical algorithms by PC level computer was required to treatment intraluminal lesions by high dose rate brachytherapy system. The exposure rate from sources was calculated with Sievert integral and dose rate in tissue was calculated with Meisberger equation, An algorithm for generating a treatment plan with optimized dose distribution was developed for high dose rate intraluminal radiotherapy. The treatment volume becomes the locus of the constrained target surface points that is the specified radial distance from the source dwelling positions. The treatment target volume may be alternately outlined on an x-ray film of the implant dummy sources. The routine used a linear programming formulism to compute which dwell time at each position to irradiate the constrained dose rate at the target surface points while minimizing the total volume integrated dose to the patient. The exposure rate and the dose distribution to be confirmed the result of calculation with algorithm were measured with film dosimetry, TLD and small size ion chambers.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1056-1064
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• 2007

Biomolecular computing is a new computing paradigm that uses biomolecules such as DNA for information representation and processing. The huge number of molecules in a small volume and the innate massive parallelism inspired a novel computation method, and various computation models and molecular algorithms were developed for problem solving. In the meantime, the use of biomolecules for information processing supports the possibility of DNA computing as an application for biological problems. It has the potential as an analysis tool for biochemical information such as gene expression patterns. In this context, a DNA computing-based model of a biomolecular perceptron has been proposed and the result of its experimental implementation was presented previously. The weight encoding and weighted sum operation, which are the main components of a biomolecular perceptron, are based on the competitive hybridization reactions between the input molecules and weight-encoding probe molecules. However, thermodynamic symmetry in the competitive hybridizations is assumed, so there can be some error in the weight representation depending on the probe species in use. Here we suggest a generalized model of hybridization reactions considering the asymmetric thermodynamics in competitive hybridizations and present a weight encoding method for the reliable implementation of a biomolecular perceptron based on this model. We compare the accuracy of our weight encoding method with that of the previous one via computer simulations and present the condition of probe composition to satisfy the error limit.