Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests

박호연,김경재 한국지능정보시스템학회 2018 지능정보연구 Vol.24 No.4

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

Simulated Annealing을 이용한 추계적 레이더 빔 스케줄링 알고리즘

노지은(Ji-Eun Roh),안창수(Chang-Soo Ahn),김선주(Seon-Joo Kim),장대성(Dae-Sung Jang),최한림(Han-Lim Choi) 한국전자파학회 2012 한국전자파학회논문지 Vol.23 No.2

능동 위상 배열 레이더(AESA Radar: Active Electronically Scanned Array radar)는 전자적으로 빔을 조향함으로써 빔 조향 시간이 비약적으로 빨라져 기존의 기계식 빔 조향 레이더에 비해 레이더에서 수행할 수 있는 다중 임무 처리 능력이 크게 향상되었다. 이러한 이유로 레이더에 주어진 시간, 에너지, 처리 능력 등의 한정된 자원을 실시간으로 효율적으로 관리, 운용할 수 있는 레이더 자원 관리 기술의 중요성이 크게 대두되었다. 그 중 레이더 빔 스케줄링 기술은 레이더 자원 관리의 핵심적인 요소라 할 수 있다. 본 논문에서는 simulated annealing을 이용한 추계적 레이더 빔 스케줄링 알고리즘을 제안하고, 이를 기존의 dispatching rule에 기반한 빔 스케줄링 기법과 비교하였다. 빔 처리 지연도(latency)와 주어진 시간 내에서 처리할 수 있는 빔의 개수 측면에서 스케줄링 결과를 비교하여 성능의 우월성을 입증하였으며, 또한 실시간성을 보장하면서도 기존의 규칙 기반 알고리즘보다 성능이 우수함을 보였다. AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability, compared with mechanically scanned array radar. AESA radar brings a new challenges, radar resource management(RRM), which is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed stochastic radar beam scheduling algorithm using simulated annealing(SA), and evaluated the performance on the multi-function radar scenario. As a result, we showed that our proposed algorithm is superior to previous dispatching rule based scheduling algorithm from the viewpoint of beam processing latency and the number of scheduled beams, with real time capability.

데이터 클러스터링을 위한 혼합 시뮬레이티드 어닐링

김성수(Sung-Soo Kim),백준영(Jun-Young Baek),강범수(Beom-Soo Kang) 한국산업경영시스템학회 2017 한국산업경영시스템학회지 Vol.40 No.2

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

The evolution of microstructure and resistance in electroplated copper films by linear integrated laser scanning annealing

Lingyue Tan,Silin Han,Shuhui Chen,Tao Hang,Huiqin Ling,Yunwen Wu,Ming Li 대한금속·재료학회 2021 ELECTRONIC MATERIALS LETTERS Vol.17 No.2

Thermal treatment is an effective way to decrease the resistivity and internal stress by inducing grain growth accompaniedwith redistribution of embedded impurities. With the narrowing of Cu interconnects in IC packaging, the increased resistanceis becoming the main issue that hindering the electrical performance of IC. Herein, a laser annealing method by linerscanning (LALS) to anneal the Cu interconnects were reported which provide a gradient thermal field for the crystallographic/microstructure transition. The impacts of laser annealing on the sheet resistance of the electroplating Cu films wereinvestigated in aspects of microstructure and phase field simulation. Cu films treated by LALS owned larger average grainsize, better recrystallization fraction, and significantly higher average grain aspect ratio than conventional annealing, whichindicated the increased driving force for grain boundaries evolution by LALS method. This study exhibited the direct evidenceon the impacts of laser annealing process on the resistance of electroplated Cu films. The laser annealing process witha local temperature gradient caused a significant decline in Cu electrical resistance compared to the conventional annealingprocess, indicating its extraordinary potential in improving Cu wire conductivity. This work will provide a scientific basis forselecting the post-treatment process for electrodeposited Cu films to achieve ideal electrical properties and microstructurein electronics industry applications.

Sequence-Pair 기반의 플로어플랜을 위한 개선된 Simulated-Annealing 기법

성영태(Young-Tae Sung),허성우(Sung-Woo Hur) 대한전자공학회 2009 電子工學會論文誌-SD (Semiconductor and devices) Vol.46 No.4

Sequence-Pair(SP) 모델은 모듈간의 위상 관계를 표현하는 방법으로써, 일반적으로 SP 모델에 기반한 플로어플래너(floorplanner)는 Simulated-Annealing(SA) 알고리즘을 통해 해를 탐색한다. 다양한 논문에서 SP와 SA 기반 배치 알고리즘의 성능 향상을 위해 SP의 평가 함수의 개선, SA의 스케줄링 기법 향상과 변형 함수의 개선 등을 모색하였다. 제안 기법은 기존의 SA 프레임웍을 수정한 2단계 SA 알고리즘으로써, 전 단계에선 SP로부터 구한 플로어플랜에 압축기법을 적용하여 모듈 사이에 존재하는 빈 공간을 가능한 최소화시켰다. 압축기법이 적용된 플로어플랜으로터 SP를 얻고, 이를 변환함으로써 해 공간을 탐색해 간다. 해가 기준 값에 수렴되었다고 판단되면 전 단계의 SA 기반 검색을 중단하고 압축기법을 사용하지 않은 기존의 SA 프레임웍을 이용하여 최적 해를 계속 탐색해 간다. MCNC 벤치마크 회로를 이용한 실험을 통해 제안 기법이 SA의해 탐색 과정에 끼치는 효과를 보이며, 제안 기법을 통해 얻은 결과가 기존의 SA 기반 알고리즘으로 구한 결과보다 우수함을 보인다. Sequence-Pair(SP) model represents the topological relation between modules. In general, SP model based floorplanners search solutions using Simulated-Annealing(SA) algorithm. Several SA based floorplanning techniques using SP model have been published. To improve the performance of those techniques they tried to improve the speed for evaluation function for SP model, to find better scheduling methods and perturb functions for SA. In this paper we propose a two phase SA based algorithm. In the first phase, white space between modules is reduced by applying compaction technique to the floorplan obtained by an SP. From the compacted floorplan, the corresponding SP is determined. Solution space has been searched by changing the SP in the SA framework. When solutions converge to some threshold value, the first phase of the SA based search stops. Then using the typical SA based algorithm, ie, without using the compaction technique, the second phase of our algorithm continues to find optimal solutions. Experimental results with MCNC benchmark circuits show that how the proposed technique affects to the procedure for SA based floorplainning algorithm and that the results obtained by our technique is better than those obtained by existing SA-based algorithms.

Through-Silicon Via를 활용한 3D NAND Flash Memory의 전열 어닐링 발열 균일성 개선

손영서,이광선,김유진,박준영 한국전기전자재료학회 2023 전기전자재료학회논문지 Vol.36 No.1

This paper demonstrates a novel NAND flash memory structure and annealing configuration including through-silicon via (TSV) inside the silicon substrate to improve annealing efficiency using an electro-thermal annealing (ETA) technique. Compared with the conventional ETA which utilizes WL-to-WL current flow, the proposed annealing method has a higher annealing temperature as well as more uniform heat distribution, because of thermal isolation on the silicon substrate. In addition, it was found that the annealing temperature is related to the electrical and thermal conductivity of the TSV materials. As a result, it is possible to improve the reliability of NAND flash memory. All the results are discussed based on 3-dimensional (3-D) simulations with the aid of the COMSOL simulator.

Optimized Local Relocation for VLSI Circuit Modification Using Mean-Field Annealing

Gholam Reza Karimi,Ahmad Azizi Verki,Sattar Mirzakuchaki 한국전자통신연구원 2010 ETRI Journal Vol.32 No.6

In this paper, a fast migration method is proposed. Our method executes local relocation on a model placement where an additional module is added to it for modification with a minimum number of displacements. This method is based on mean-field annealing (MFA), which produces a solution as reliable as a previously used method called simulated annealing. The proposed method requires substantially less time and hardware, and it is less sensitive to the initial and final temperatures. In addition, the solution runtime is mostly independent of the size and complexity of the input model placement. Our proposed MFA algorithm is optimized by enabling module rotation inside an energy function called permissible distances preservation energy. This, in turn, allows more options in moving the engaged modules. Finally, a three-phase cooling process governs the convergence of problem variables called neurons or spins.

크러스터링을 위한 타부서치 및 시뮬레이티드 애닐링 기법의 결합

이상복 서경대학교 1996 論文集 Vol.24 No.-

Clustering or classification is a very fundamental task that may occur almost everywhere for the purpose of grouping. Optimal clustering is an example of very complicated combinatorial optimization problem and it is hard to develop a generally applicable optimal algorithm. In this paper we propose a general-purpose algorithm for the optimal clustering based on·simulated annealing. Among various iterative global optimization techniques imitating natural phenomena that have been proposed and utilized successfully for various combinatorial optimization problems, simulated annealing has its superiority because of its convergence property and simplicity. We first present a version of algorithm combined tabu search and simulated annealing(ATS) and then we apply ATS to clustering Problem. An qualitative and quantitative examples are given.

An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

Shouvik Chakraborty,Sandeep Bhowmik 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11

The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

전역최적화 알고리즘과 유한요소모델을 이용한 용접열원 파라미터 추정에 관한 연구 Part I : Adaptive Simulated Annealing의 적용

표창민 한국기계기술학회 2020 한국기계기술학회지 Vol.22 No.3

Anticipation of welding deformation with finite element method is a very interested topic in the industries, adequate heat source model is essential for concluding reasonable results. This study is related to estimate the parameters of Goldak heat source model, and global optimization algorithm is applied to this research. The heat affected zone (HAZ) boundary line of bead on plate (BOP) welding is used as the target, parameters of heat sources are used as the variables. Adaptive simulated annealing is applied and the optimal result is obtained out of 1,000 candidates. The convergence of finite element method and the global optimization is meaningful for estimation of welding deformation, which could enhance to reduce the resources and time for experiments.