• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.1
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• pp.9-18
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• 2003

In this paper, we analyzed the throughput of DS/CDMA system applying packet combining scheme combined with Type- I Hybrid ARQ scheme over AWGN channel and Nakagami fading channel with RAKE receiver. As the parameter for analysis, we used number of combined packet(L), number of diversity branch$({L_c})$, fading index(m), and length of packet(N), and used CRC-12 error detection code and (2,1,3) convolutional code. As a results, we found that throughput of system over Nakagami fading channel with RAKE receiver was superior to throughput over AWGN channel, and throughput of system decreases rapidly as channel degrades when number of combined packet(L) was increased. However throughput of system with the combining scheme was achieved even at low ${E_b}/{N_o}$. Also, we found that throughput of system was increased when fading index(m) and number of diversity branch$({L_c})$ were increased, but it was decreased when number of user(K) and length of packet(N) were increased.

• Journal of Sensor Science and Technology
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• v.9 no.5
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• pp.334-340
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• 2000

This paper presents the fabrication of a micro electromagnetic flow sensor for the liquid flow rate measurement. The micro electromagnetic flow sensor has some advantages such as a simple structure, no heat generation, a rapid response and no pressure loss. The principle of the micro electromagnetic flow sensor is based on Faraday's law. If conductive fluid passes through a magnetic field, the electromotive force is generated and detected by two electrodes on the wall of the flow channel. The flow sensor consists of two permanent magnets and a silicon flow channel with two electrodes. The dimension of the flow sensor is $9\;mm\;{\times}\;9\;mm\;{\times}\;1\;mm$. The micro flow channel is mainly fabricated by anisotropic etching of two silicon wafers, and the detection electrodes are fabricated by metal evaporation process. The characteristic of the fabricated flow sensor is obtained experimentally. When the flow rates of water with the conductance of $100-200\;{\mu}S/cm$ are 9.1 ml/min and 62 ml/min, the generated electromotive forces are $261\;{\mu}V$ and 7.3 mV, respectively.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.19-34
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• 2015

One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.99-107
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• 2023

Recently, the rate of death and safety accidents at construction sites is the highest among all kinds of industries. In order to apply artificial intelligence technology to construction sites, it is essential to secure a dataset which can be used as a basic training data. In this paper, a number of image data were collected through actual construction site, for which major construction equipment objects mainly operated in civil engineering sites were defined. The optimal training dataset construction was completed by annotation process of about 90,000 image dataset. Reliability of the dataset was verified with the mAP of over 90 % in use of YOLO, a representative model in the field of object detection. The construction equipment training dataset built in this study has been released which is currently available on the public data portal of the Ministry of Public Administration and Security. This dataset is expected to be freely used for any application of object detection technology on construction sites especially in the field of construction safety in the future.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.57-64
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• 2005

This paper presents a built-in current sensor(BICS) that detects defects in CMOS integrated circuits using the current testing technique. This circuit employs a cross-coupled connected PMOS transistors, it is used as a current comparator. The proposed circuit has a negligible impact on the performance of the circuit under test (CUT) and high speed detection time. In addition, in the operation of the normal mode, the BlCS does not have dissipation of extra power, and it can be applied to the deep submicron process. The validity and effectiveness are verified through the HSPICE simulation on circuits with defects. The area overhead of a BlCS versus the entire chip is about 9.2%. The chip was fabricated with Hynix $0.35{\mu}m$ 2-poly 4-metal N-well CMOS standard technology.

• Laser Solutions
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• v.15 no.1
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• pp.6-9
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• 2012

In TFT-LCD manufacturing process, various defects are generated by manufacturing machine trouble or particle. These defects can be repaired through the TFT-Laser repair process that only can't be automated in TFT-LCD manufacturing Process. In this Paper, we propose auto defect algorithm for TFT-LCD laser repair machine using image processing algorithm in order to automate process. Proposed algorithm can detect very small defects (< 2um) in 98% success ratio, and generated laser repair path guarantee highly precise position accuracy. Through proposed system, much of the work still done the old-fashioned way, by hand, can be automated and manufacturing company can be strengthed the competitiveness of cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3885-3906
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• 2020

Hybrid fuzzing which combines fuzzing and concolic execution, has proved its ability to achieve higher code coverage and therefore find more bugs. However, current hybrid fuzzers usually suffer from inefficiency and poor scalability when applied to complex, real-world program testing. We observed that the performance bottleneck is the inefficient cooperation between the fuzzer and concolic executor and the slow symbolic emulation. In this paper, we propose a novel solution named EPfuzzer to improve hybrid fuzzing. EPfuzzer implements two key ideas: 1) only the hardest-to-reach branch will be prioritized for concolic execution to avoid generating uninteresting inputs; and 2) only input bytes relevant to the target branch to be flipped will be symbolized to reduce the overhead of the symbolic emulation. With these optimizations, EPfuzzer can be efficiently targeted to the hardest-to-reach branch. We evaluated EPfuzzer with three sets of programs: five real-world applications and two popular benchmarks (LAVA-M and the Google Fuzzer Test Suite). The evaluation results showed that EPfuzzer was much more efficient and scalable than the state-of-the-art concolic execution engine (QSYM). EPfuzzer was able to find more bugs and achieve better code coverage. In addition, we discovered seven previously unknown security bugs in five real-world programs and reported them to the vendors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2037-2051
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• 2012

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

• Proceedings of the IEEK Conference
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• summer
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• pp.82-85
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• 2004

This paper proposes an advanced circuit for the capacitive type fingerprint sensor signal processing and an effective isolation structure for minimizing an electrostatic discharge(ESD) influence and for removing a signal coupling noise of each sensor pixel. The proposed detection circuit increases the voltage difference between a ridge and valley about $80\%$ more than old circuit. The test chip is composed of $160\;\times\;192$ array sensing cells $(9,913\times11,666\;um^2).$ The sensor plate area is $58\;\times\;58\;um^2$ and the pitch is 60um. The image resolution is 423 dpi. The chip was fabricated on a 0.35um standard CMOS process. It successfully captured a high-quality fingerprint image and performed the registration and identification processing. The sensing and authentication time is 1 sec(.) with the average power consumption of 10 mW at 3.0V. The reveal ESD tolerance is obtained at the value of 4.5 kV.