• Journal of Power Electronics
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• v.19 no.6
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• pp.1566-1574
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• 2019

Maximum power point tracking (MPPT) under the partial shading condition is a challenging research topic for photovoltaic systems. Shaded photo-voltaic module result in complex peak patterns on the power versus voltage curve which can misguide classical MPPT algorithms. Thus, various kinds of global MPPT algorithms have been studied. These have typically consisted of partial shading detection, global peak search and MPPT. The conventional partial shading detection algorithm aims to detect all of the occurrences of partial shading. This results in excessive execution of global peak searches and discontinuous operation of the MPPT. This in turn, reduces the achievable power for the PV module. Based on a theoretical investigation of power verse voltage curve patterns under various partial shading conditions, it is realized that not all the occurrences of partial shadings require a global peak search. Thus, an intelligent partial shading detection algorithm that provides exact identification of global peak search necessity is essential for the efficient utilization of solar energy resources. This paper presents a new partial shading determinant algorithm utilizing adaptive threshold levels. Conventional methods tend to be too sensitive to sharp shading patterns but insensitive to smooth patterns. However, the proposed algorithm always shows superb performance, regardless of the partial shading patterns.

• Journal of Environmental Health Sciences
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• v.30 no.1
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• pp.15-21
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• 2004

In an attempt to analyze the characteristics of domestic pathogenic strains of S. pneumoniae, the basic epidemiological charactristics of pathogenic strains such as their serotypes and frequency of penicillin resistance, and pattern of chromosomal DNA from PFGE(pulsed-field gel electrophoresis) were observed. For this study,56 strains of S. pneumoniae isolated from inpatients and outpatients in the four domestic university hospitals were collected from January to December in 1998. Among those strains, a total of 56 pathogenic strains from blood(39 isolates), cerebrospinal fluid(8 isolates) and other specimen(9 isolates) were selected and isolated. The penicillin resistance frequency of those 56 strains was identified with disk diffusion method with 66.1％. From the invasive strains, predominant serotypes were isolated in the order of 19F(12.5％), 23F(10.7%), 14(10.7%) and 9V(10.7％), totalling 45 percent. This experiment also used PFGE patterns to compare the correlations among genetic subtypes in several serotypes. The DNA fragments digested with Sma I and Apa I were resolved by PFGE. The PFGE patterns digested with Sma I were better than Apa I for analysis. In the DNA fragments digested with Sma 1, PFGE analysis of 56 S. pneumoniae isolates showed 25 different patterns. As a result, serotype was on the whole correlated to PFGE pattern on the ground that each different PFGE pattern by serotype was observed. This study can be utilized not only fur the study of incidence trend of domestic pneumococcal diseases but also as a useful basic data for the development of identification tool and treatment.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.63-67
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• 2000

This paper deals with text-prompt speaker recognition based on dynamic time warping (DTW). The Genetic Algorithm was applied to the creation of reference patterns for suitable reflection of the speaker characteristics, one of the most important determinants in the fields of speaker recognition. In order to overcome the weakness of text-dependent and text-independent speaker recognition, the text-prompt type was suggested. Performed speaker identification and verification in close and open set respectively, hence the Genetic algorithm-based reference patterns had been proven to have better performance in both recognition rate and speed than that of conventional reference patterns.

• Biomedical Science Letters
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• v.8 no.1
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• pp.39-46
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• 2002

Amplified fragment length polymorphism (AFLP) is a recently developed PCR-based high resolution fingerprinting method that is able to generate complex banding patterns which can be used to delineate intraspecific genetic relationships among bacteria. In this study, we have modified and evaluated a PCR-based technique, amplified fragment length polymorphism (AFLP) analysis, for use in fingerprinting strains of Staphylococcus aureus. Single-enzyme amplified fragment length polymorphism (SE-AFLP) analysis was used to perform strain identification of Staphylococus aureus. By careful selection of AFLP primers, it was possible to obtain reproducible and sensitive identification to strain level. AFLP fingerprinting of 5 reference strains of Staphylococcus aureus and 65 strains of Staphylococcus aureus that were isolated from food sources of different area and diverse genomic types of Staphylococcus aureus were recognized. As a result of this study, we found that the AFLP patterns of Staphylococcus aureus isolated from Seoul, Taejeon and Gwang-Ju indicated the close relation with genetic similarity. The main purpose of this study was to find an alternative and reliable fingerprinting method to study the overall genetic diversity, using Staphylococcus aureus species as an example, and observed if the method can be successfully applied to all staphylococcal species.

• Journal of Microbiology
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• v.35 no.3
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• pp.172-176
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• 1997

The Mycobacterium tuberculosis clinical isolates in Korea, showing different drug resistances, were analyzed by comparing large restriction fragment (LRF) patterns produced y digestion of genomic DNA with infrequent-cutting endonucleases of SpeI, AsnI and pulsed-field gel electrophoresis (PFGE). SpeI and AsnI allowed with AsnI and SpeI, strains yielded an absolutely identical pattern for Korean type's mycobacteria even though they showed different drug resisstance. However, when three M. tuberculosis strains, showing drug resistance, were digested with XbaI, patterns were different from those of the other M. tuberculosis strians which are susceptible to drugs. This stuyd reveals that the comparison of chromosomal restriction patterns is very useful as an additional aid for the differentiation and identification of M. tuberculosis strains showing drug resistances.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.968-974
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• 2006

We are moving into the era of ubiquitous computing. Ubiquitous Sensor Network (USN) is a base of such computing paradigm, where recognizing the identification and the position of objects is important. For the object identification, RFID tags are commonly used. For the object positioning, use of sensors such as laser and ultrasonic scanners is popular. Recently, there have been a few attempts to apply RFID technology in robot localization by replacing the sensors with RFID readers to achieve simpler and unified USN settings. However, RFID does not provide enough sensing accuracy for some USN applications such as robot navigation, mainly because of its inaccuracy in distance measurements. In this paper, we describe our approach on achieving accurate navigation using RFID. We solely rely on RFID mechanism for the localization by providing coordinate information through RFID tag installed floors. With the accurate positional information stored in the RFID tag, we complement coordinate errors accumulated during the wheel based robot navigation. We especially focus on how to distribute RFID tags (tag pattern) and how many to place (tag granularity) on the RFID tag-floor. To determine efficient tag granularities and tag patterns, we developed a simulation program. We define the error in navigation and use it to compare the effectiveness of the navigation. We analyze the simulation results to determine the efficient granularities and tag arrangement patterns that can improve the effectiveness of RFID navigation in general.

• Fisheries and Aquatic Sciences
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• v.3 no.2
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• pp.83-87
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• 2000

We have investigated lectin binding patterns in order to apply binding records of previous laboratory experiments to field settings before the first ourbreaks of harmful algal bloom (HAB). Although cells were grown under different conditions, the binding patterns were the same as in the control. In addition, culture days was not associated with the binding patterns, when compared with the control. In nature, this results suggest that ECA, HPA and WGA lectin are able to discriminate between C. polykrikoides and G. impudicum, as well as ECA and SBA have a capability as a tool for differentiating between C. polyrikoides and G. catenatum, although these species are closely similar under the light microscope fiexed with Lugol solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.603-614
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• 2013

The popularity of mobile devices provides their users with a circumstance that services and information can be accessed wherever and whenever users need. Accordingly, various studies have been proposed personalized methods to improve accessibility of mobile users to information. However, since these personalized methods require users' private information, they gives rise to problems on security. An efficient way to resolve security problems is to estimate user information by using their online and offline behavior. In this paper, for this purpose, it is proposed a novel user information identification system that identifies users' personal and social information by using both his/her behavior on social network services and proximity patterns obtained from GPS data. In the proposed system, personal information of a user like age, gender, and so on is estimated by analyzing SNS texts and POI (Point of Interest) patterns, while social information between a pair of users like family and friend is predicted with proximity patterns between the users. Each identification module is efficiently designed to handle the characteristics of user data like much noise in SNS texts and missing signals in GPS data. In experiments to evaluate the proposed system, our system shows its superiority against ordinary identification methods. This result means that the proposed system can efficiently reflect the characteristics of user data.