• Computers and Concrete
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• 제12권6호
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• 보건행정학회지
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• 제13권3호
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• pp.35-51
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• 2003

This study purports to develop a quality assessment tool for the process of health promotion programs at public health centers(PHC). The draft of the assessment tool developed by the literature was distributed to 242 staffs who were in charge of the health promotion programs at PHCs for evaluating the feasibility of the tool on September and October 2002. The major results of the study were as follows; The quality assessment tool developed in the study consisted of four domains: strategic planning, program management, monitoring and evaluation, and resources and information. The strategic planning dealt with the function of the planning staff and committees, community data analysis, the feasibility of the program, and the approach methods for attaining the goal of the program. The program management included the items on the qualification and power of the program staff. The monitoring and evaluation included the items on the reporting and communication among program units, and feed back after monitoring. Finally, the resources and information dealt with community networking, clients' response, and consulting activity of the staff. The validity of the tools was tested and partly supported by both formative and criterion-related methods. The assessment tools developed in this study could be used by health promotion workers in the self-evaluation of the program quality. In conclusion, the quality assessment tool developed in the study will be a good safeguard for assuring the quality of the process of health promotion programs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.692-710
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• 2013

Conventional interference alignment (IA) for a MIMO interference channel (IFC) requires global and perfect channel state information at transmitter (CSIT) to achieve the optimal degrees of freedom (DoF), which prohibits practical implementation. In order to alleviate the global CSIT requirement caused by the coupled relation among all of IA equations, we propose an IA scheme with a single feedback link of each receiver in a limited feedback environment for a three-user MIMO IFC. The main feature of the proposed scheme is that one of users takes out a fraction of its maximum number of data streams to decouple IA equations for three-user MIMO IFC, which results in a single link feedback structure at each receiver. While for the conventional IA each receiver has to feed back to all transmitters for transmitting the maximum number of data streams. With the assumption of a random codebook, we analyze the upper bound of the average throughput loss caused by quantized channel knowledge as a function of feedback bits. Analytic results show that the proposed scheme outperforms the conventional IA scheme in term of the feedback overhead and the sum rate as well.

• Advances in Computational Design
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• 제3권3호
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• pp.289-302
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• 2018

The use of optimum content of supplementary cementing materials (SCMs) such as limestone filler (LF) to blend with Portland cement has been resulted in many environmental and technical advantages, such as increase in physical properties, enhancement of sustainability in concrete industry and reducing $CO_2$ emission are well known. Artificial neural networks (ANNs) have been already applied in civil engineering to solve a wide variety of problems such as the prediction of concrete compressive strength. The feed forward back propagation (FFBP) algorithm and Tan-sigmoid transfer function were used for the ANNs training in this study. The training, testing and validation of data during the backpropagation training process yielded good correlations exceeding 97%. A parametric study was conducted to study the sensitivity of the developed model to certain essential parameters affecting the compressive strength of concrete. The effects and benefits of limestone filler on hardened properties of the concrete such as compressive strength were well established endorsing previous results in the literature. The results of this study revealed that the proposed ANNs model showed a high performance as a feasible and highly efficient tool for simulating the LF concrete compressive strength prediction.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.1019-1022
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• 2002

Pipe-cutting machines have been used in many fields. Recently, an automatic pipe-cutting machine that uses magnet has born developed. In this paper, a magnetic-type automatic pipe-cutting machine that attaches itself and performs unmanned cutting process is proposed. It is designed that there is a room at the bottom of its body to contain a magnet. And it uses magnetic force between the magnet and the pipe surface to prevent slip and to attach the machine to the pipe against gravity. Also the magnetic force is adjustable by changing the gap between the magnet and the pipe. This machine is, however, necessary to control cutting velocity for the elevation of work efficiency and the adjustable faculties. During pipe cutting process, the gravity acting on the pipe-cutting machine widely varies. That is, the cutting machine gets fast when moving from the top to the bottom of the pipe and slow when moving from the bottom to the top. Actually the system is kind of a non-linear system where the gravity is function of climbing angle of the cutting machine along the pipe. Especially jerking motion is critical. Therefore, authors design the non-linear controller that estimates the current position of the machine along the pipe and compensates the effect of gravity in this paper. It receives the feed back signal from the encoder.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.210-213
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• 1999

본 논문에서는 로봇 매니퓰레이터 제어를 위해 새로운 신경회로망을 제안한다. 제안한 신경회로망구조는 은닉층과 출력층의 출력이 피드백 층을 거쳐 다시 은닉층과 출력층으로 피드백되는 구조이다. 피드백 층은 한번의 시간 지연을 갖는다. 제안한 신경회로망의 학습은 일반적인 오차 역전파 알고리즘을 사용한다. 로봇 매니퓰레이터를 대상으로 시뮬레이션과 실험을 통해서 제안한 신경회로망 구조의 유용성을 확인한다.

• 한국연소학회지
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• 제9권3호
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• pp.27-35
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• 2004

Diode laser absorption sensors are advantageous because they may provide fast, sensitive, absolute, and selective measurements of species concentration. These systems are very attractive for practical applications owing to its compactness, resonable cost, robustness, and ease of use. In addition, diode lasers are fiber-optic compatible and thus enable simultaneous measurements of multiple species along a line-of-sight. Recent advances of room-temperature, near-IR and visible diode laser sources for telecommunication, optical data storage applications make it possible to be applied for combustion diagnostics based on diode laser absorption spectroscopy. Therefore, combined with fiber-optics and high sensitive detection strategies, compact and portable sensor systems are now appearing for variety of applications. The objectives of this research are to develope a new gas sensing system and to verify feasibility of this system. Wavelength and power characteristics as a function of injection current and temperature are experimentally found out. Direct absorption spectroscopy has been demonstrated in these experiments and has a bright prospect to this diode laser system.

• 한국물환경학회지
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• 제24권1호
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.