• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.251-262
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• 2013

Bad smells are usually related to program source code, arising from bad design and programming practices. Refactoring activities are often motivated by the detection of bad smells. With the increasing adoption of Design-by-Contract (DBC) methodologies in formal software development, evidence of bad design practices can similarly be found in programs that combine actual production code with interface contracts. These contracts can be written in languages, such as the Java Modeling Language (JML), an extension to the Java syntax. This paper presents a catalog of bad smells that appear during DBC practice, considering JML as the language for specifying contracts. These smells are described over JML constructs, although several can appear in other DBC languages. The catalog contains 6 DBC smells. We evaluate the recurrence of DBC smells in two ways: first by describing a small study with graduate student projects, and second by counting occurrences of smells in contracts from the JML models application programming interface (API). This API contains classes with more than 1,600 lines in contracts. Along with the documented smells, suggestions are provided for minimizing the impact or even removing a bad smell. It is believed that initiatives towards the cataloging of bad smells are useful for establishing good design practices in DBC.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.131-133
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• 1993

In the power system state estimation, the J(x)-index test and normalized residuals $r_N$ have been used to detect the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network model using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional methods and simulation results show the good performance in the bad data identification based on the neural network under sample power system.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.716-718
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• 2004

The false alarm data in intrusion detection systems are divided into false positive and false negative. The false positive makes bad effects on the performance of intrusion detection system. And the false negative makes bad effects on the efficiency of intrusion detection system. Recently, the most of works have been studied the data mining technique for analysis of alert data. However, the false alarm data not only increase data volume but also change patterns of alert data along the time line. Therefore, we need a tool that can analyze patterns that change characteristics when we look for new patterns. In this paper, we focus on the false positives and present a framework for analysis of false alarm pattern from the alert data. In this work, we also apply incremental data mining techniques to analyze patterns of false alarms among alert data that are incremental over the time. Finally, we achieved flexibility by using dynamic support threshold, because the volume of alert data as well as included false alarms increases irregular.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.139-144
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• 2000

This paper presents development of meander monitoring system base reliable detection at conveyer belt used for materials transport line of steel company. Conventional detection method is losed the confidence, because of the place with bad surroundings of measurement so much that materials production line are completely exposed to dust, moisture and vibration. For the solution of this problem, we developed infrared meander detection system using the infrared sensor array and one chip microprocessor which is available for bad surroundings and inexpensive. The reliability of the system was estimated by experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5471-5478
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• 2012

In this paper, we propose a new short-circuit detecting method which can detect bad short-circuits, one of bad types occurring in PCB(Printed Circuit Board), by using SURF(Speeded-Up Robust Features) algorithm. The basic procedure in the proposed method sequentially consists of extracting features from both sample and inputted images by SURF, performing perspective transform by feature matching and matching results, extracting check areas of interest, binary coding and extracting short-circuits, and verifying results. The proposed method focuses on the robustness which can detect bad short-circuits even though the position and angle of PCB are not uniform and arbitrarily placed. Experimental results show that our method enables to detect bad short-circuits regardless of the location and angle of PCB placed variously and validate that the proposed method outperforms the conventional methods detecting bad short-circuits manually on the aspect of both the detection rate and time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.3
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• pp.103-109
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• 2003

The validity of measured data is fundamental factor for the power system automation Measured values could have errors that are caused by the communication errors and malfunctioning measuring devices. The accuracy and reliability of measured values at a substation is an important condition for robust and fault tolerant automata. Errors can be reduced by state estimation, however, global reliability of state estimation goes down in case of the existence of some bad data In this paper, a least square state estimation and bad sensor detection algorithm based on chi-square theory, ale proposed and it is applied to a domestic 154kV distribution substations. A simulator together with user friendly graphic users interface is developed using C language and Visual Basic. TCP/IP is equipped for future connection with other operation systems.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.130-132
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• 1996

The squirrel cage rotors for induction motors may have several faults such as broken bars, bad spots in the end ring and abnormal skew caused by improper processing. These faults have bad effect on the performance of the induction motor. In this paper, these fault detection method is proposed, then the effects on the performance of the motor is analyzed using FEM.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.04c
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• pp.54-57
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• 2008

A new modular Lateral Information Propagation Networks can be implemented in a IC chip with the circuit VLSI technology for detection of bad materials. The proposed modular architecture is propagated the neural network through inter module connections. For such inter module connections, the host(computer or logic) mediates the exchange of information among modules. Also border nodes in each module have capacitors for temporarily retaining the information from outer modules. For detecting of Faulty Insulator, $4\;{\times}\;4$ neural network modules has been designed and simulation of interpolation with the designed networks has been done.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.116-118
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• 2000

The modern power system including lots of measuring devices and controller is large and complex total information system. A Lot of data and system information are transmitted to operators, and analysing these information and system management is very important. Recently, GUI(Graphic Users Interface) is emphasized as a method that operators carry out their duties, effectively. In this paper. a simulator that can show state estimation and detection of bad measuring devices is introduced for domestic 154kV/22.9kV distribution substations. C language and Visual Basic is used for this simulator. and TCP/IP is adopted to consider connection with a Power system.