• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.79-85
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• 2007

The modem industry age began when the conveyer system was introduced by Ford to produce model "T". The conveyer system is designed to optimize and maximize mass production of a specific item. Nowadays, however, accommodating to individual tastes has become an important factor in selection of products. Thus, rather than the mass production of one item, producing fewer but a wide variety of goods became important. To give flexibility and elasticity to the conveyer system, a new method of transportation where it is possible to choose a specific item is necessary. Therefore mall quantity and high-volume mass production was decrescent and small quantity batch production was expanded. In this paper, we developed multiple crate stacking and picking system to give flexibility to the conveyer system. First, we verified the conceptually designed system through manufacture. Second, we solved the problems that would happen on the actual field using pneumatic system. Finally, we optimized the system through FEM technique. This system works with stability and fast speed and can improve work efficiency which would minimize the losses resulting from too much dependence on manual labor.

• Journal of the Korean Chemical Society
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• v.57 no.3
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• pp.316-321
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• 2013

Electrocoagulation (EC) technique is applied for the treatment of wastewater containing heavy metals ions such as nickle (Ni), lead (Pb) and cadmium (Cd) by using sacrificial anodes corrode to release active coagulant flocs usually aluminium or iron cations into the solution. During electrolytic reactions hydrogen gas evolve at the cathode. All the experiments were carried out in Batch mode. The tank was filled with synthetic wastewater containing heavy metals and efficiency of electro-coagulation in combination with aluminum and iron electrodes were investigated for removal of such metals. Several parameters, such as contact time, pH, electro-coagulant concentration, and current density were optimized to achieve maximum removal efficiency (%). The concentrations of heavy metals were determined by using Atomic Absorption Spectroscopy (AAS). It is found that the electro-coagulation process has potential to be utilized for the cost-effective removal of heavy metals from wastewater specially using iron electrodes in terms of high removal efficiencies and operating cost.

• Journal of Microbiology and Biotechnology
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• v.22 no.12
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• pp.1758-1766
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• 2012

An extensive investigation was carried out to describe the kinetics of cell growth, substrate consumption, and product formation in the batch fermentation using starch as substrate. Evaluation of intrinsic kinetic parameters was carried out using a best-fit unstructured model. A nonlinear regression technique was applied for computational purpose. The Andrew's model showed a comparatively better $R^2$ value among all tested models. The values of specific growth rate (${\mu}_{max}$), saturation constant ($K_S$), inhibition constant ($K_I$), and $Y_{X/S}$ were found to be 0.109 $h^{-1}$, 11.1 g/l, 0.012 g/l, and 1.003, respectively. The Leudeking-Piret model was used to study the product formation kinetics and the process was found to be growth-associated. The growth-associated constant (${\alpha}$) for protease production was sensitive to substrate concentration. Its value was fairly constant up to a substrate concentration of 30.8 g/l, and then decreased.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.399-408
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• 2002

Component technology was proposed and applied to software development to overcome software crisis. Software component is a black box like an integrated circuit in hardware but it can not be utilized without good support specially for helping users understand efficiently. This paper shows that data book format for understanding hardware component can be well applied to representing software component. We selected an approach to understand component by matching the contents of data book with UML and API model technique. Besides, we added the architecture part and the interface which are the most important property of software component to the data book for software components. In order to verify effectiveness of components data book we extended batch descriptor in EJB and performed an experiment providing data book to programmers with components.

• Korean Journal of Materials Research
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• v.4 no.7
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• pp.808-816
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• 1994

The work present here shows the technique to produce p-Doped polypyrrole particles by semibatch dispersion polymerization using steric-stabilizer. Monomer-starved polymerization process was successful to increase the particle size up to 50 nm in case of polyvinylalcohol (PVA) stabilizer, and up to 95 nm in case of methylcellulose stabilizer. The particle size and the bulk conductivity changed with the feed rates of monomer, the concentrations of initiator (dopant) and the type, molecular weight, concentrations of steric-stabilizer.

• Environmental Engineering Research
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• v.24 no.2
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• pp.354-361
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• 2019

Renewable energy from biomass and biodegradable wastes can significantly supplement the global energy demand if properly harnessed. Pyrolysis is the most profound modern technique that has proved effective and efficient in the energy conversion of biomass to yield various products like bio-oil, biochar, and syngas. This study focuses on optimization of slow pyrolysis of banana peels waste to yield banana peels vinegar, tar and biochar as bio-infrastructure products. Response surface methodology using central composite design was used to determine the optimum conditions for the banana wastes using a batch reactor pyrolysis system. Three factors namely heating temperature ($350-550^{\circ}C$), sample mass (200-800 g) and residence time (45-90 min) were varied with a total of 20 individual experiments. The optimal conditions for wood vinegar yield (48.01%) were $362.6^{\circ}C$, 989.9 g and 104.2 min for peels and biochar yield (30.10%) were $585.9^{\circ}C$, 989.9 g and 104.2 min. The slow pyrolysis showed significant energy conversion efficiencies of about 90% at p-value ${\leq}0.05$. These research findings are of primary importance to Uganda considering the abundant banana wastes amounting to 17.5 million tonnes generated annually, thus using them as pyrolysis feedstock can boost the country's energy status.

• Journal of Electrochemical Science and Technology
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• v.14 no.2
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• pp.174-183
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• 2023

The dye wastewater discharge from the textile industries mainly affects the aquatic environment. Hence, the treatment of this wastewater is essential for a pollutant-free environment. The purpose of this research is to optimize the dye removal efficiency for process influencing independent variables such as pH, electrolysis time (ET), and current density (CD) by using Box-Behnken design (BBD) optimization and Genetic Algorithm (GA) modelling. The electrocoagulation treatment technique was used to treat the synthetically prepared Reactive Black dye solution under batch mode potentiometric operations. The percentage of error for the BBD optimization was significantly greater than for the GA modelling results. The optimum factors determined by GA modelling were CD-59.11 mA/cm², ET-24.17 minutes, and pH-8.4. At this moment, the experimental and predicted dye removal efficiencies were found to be 96.25% and 98.26%, respectively. The most and least predominant factors found by the beta coefficient were ET and pH respectively. The outcome of this research shows GA modeling is a better tool for predicting dye removal efficiencies as well as process influencing factors.

• Journal of Microbiology and Biotechnology
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• v.33 no.4
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• pp.419-429
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• 2023

Ultraviolet C (UV-C, 200-280 nm) light has germicidal properties that inactivate a wide range of pathogenic and spoilage microorganisms. UV-C has been extensively studied as an alternative to thermal decontamination of fruit juices. Recent studies suggest that the efficacy of UV-C irradiation in reducing microorganisms in fruit juices is greatly dependent on the characteristics of the target microorganisms, juice matrices, and parameters of the UV-C treatment procedure, such as equipment and processing. Based on evidence from recent studies, this review describes how the characteristics of target microorganisms (e.g., type of microorganism/strain, acid adaptation, physiological states, single/composite inoculum, spore, etc.) and fruit juice matrices (e.g., UV absorbance, UV transmittance, turbidity, soluble solid content, pH, color, etc.) affect the efficacy of UV-C. We also discuss the influences on UV-C treatment efficacy of parameters, including UV-C light source, reactor conditions (e.g., continuous/batch, size, thickness, volume, diameter, outer case, configuration/arrangement), pumping/flow system conditions (e.g., sample flow rate and pattern, sample residence time, number of cycles), homogenization conditions (e.g., continuous flow/recirculation, stirring, mixing), and cleaning capability of the reactor. The collective facts indicate the immense potential of UV-C irradiation in the fruit juice industry. Existing drawbacks need to be addressed in future studies before the technique is applicable at the industrial scale.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.29-38
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• 2024

Wastewater management is increasingly emphasizing economic and environmental sustainability. Traditional methods in sewage treatment plants have significant implications for the environment and the economy due to power and chemical consumption, and sludge generation. To address these challenges, a study was conducted to develop the Intermittent Cycle Extended Aeration System (ICEAS). This approach was implemented as the primary technique in a full-scale wastewater treatment facility, utilizing key operational factors within the standard Sequencing Batch Reactor (SBR) process. The optimal operational approach, identified in this study, was put into practice at the research facility from January 2020 to December 2022. By implementing management strategies within the biological reactor, it was shown that maintaining and reducing chemical quantities, sludge generation, power consumption, and related costs could yield economic benefits. Moreover, adapting operations to influent characteristics and seasonal conditions allowed for efficient blower operation, reducing unnecessary electricity consumption and ensuring proper dissolved oxygen levels. Despite annual increases in influent flow rate and concentration, this study demonstrated the ability to maintain and reduce sludge production, electricity consumption, and chemical usage. Additionally, systematic responses to emergencies and abnormal situations significantly contributed to economic, technical, and environmental benefits.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.