8 research outputs found

    (An) exploratory study of anxiety factors and anxiety levels experienced by hospitalized patients in army hospitals

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    ๊ฐ„ํ˜ธํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฐ๋ณ‘์› ์ž…์›ํ™˜์ž๋“ค์ด ์ž…์›ํ•œ ์ƒํ™ฉ์—์„œ ๊ฒฝํ—˜ํ•˜๋Š” ๋ถˆ์•ˆ์˜ ์ •๋„๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ๊ทธ ์š”์ธ์„ ๊ทœ๋ช…ํ•˜๋ฏ€๋กœ์จ ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…๊ณผ ์˜ˆ๋ฐฉ์ฑ…์„ ๊ฐ•๊ตฌํ•˜๋Š” ๋ฐ ๊ธฐ์ดˆ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‹œ๋„๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์€ 1984๋…„ 4์›”24์ผ๋ถ€ํ„ฐ 5์›”4์ผ๊นŒ์ง€ ์•ฝ 11์ผ๊ฐ„์— ๊ฑธ์ณ ์ „๋ฐฉOO์ง€์—ญ์— ์œ„์น˜ํ•œ 3๊ฐœ์˜ ๊ตฐ๋ณ‘์›์— ์ž…์›ํ•˜์—ฌ ์น˜๋ฃŒ๋ฐ›๊ณ  ์žˆ๋Š” ์ž…์›ํ™˜์ž 196๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ถˆ์•ˆ์š”์ธ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋„๊ตฌ๋กœ๋Š” ์—ฐ๊ตฌ์ž๊ฐ€ 22๋ฌธํ•ญ์œผ๋กœ ๋œ ์งˆ๋ฌธ์ง€๋ฅผ ์ž‘์„ฑํ•˜๊ณ , ๋ถˆ์•ˆ์ •๋„๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋„๊ตฌ๋กœ๋Š” Zung์ด ๊ฐœ๋ฐœํ•œ The Status Anxiety-Inventory๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ๋ถ„์„ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๋ฐฑ๋ถ„์œจ, x**2๊ฒ€์ •, ํ”ผ์–ด์Šจ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„๋ฒ•, ๋‹จ๊ณ„์  ์ค‘ํšŒ๊ท€ ๋ถ„์„๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1 .์‘๋‹ต์ž ์ „์ฒด์˜ ๋ถˆ์•ˆ์š”์ธ ํ‰๊ท ์€ 2.897์ด์—ˆ๊ณ , "๊ฐ€์กฑ์˜ ๋ฌธ์ œ๊ฐ€ ์—ผ๋ ค๋  ๋•Œ"๊ฐ€ ํ‰๊ท ์ ์ˆ˜ 3.342๋กœ์„œ ๊ฐ€์žฅ ๋†’์•˜๊ณ , "์ž…์›์œผ๋กœ ์ธํ•˜์—ฌ ํœด๊ฐ€๋“ฑ์ด ์ทจ์†Œ๋  ๋•Œ"๊ฐ€ ํ‰๊ท ์ ์ˆ˜ 3.250์œผ๋กœ์„œ ๋‘ ๋ฒˆ์งธ๋กœ ๋†’์•˜์œผ๋ฉฐ "์•“๊ณ  ์žˆ๋Š” ์งˆ๋ณ‘์ด ๋ฌด์Šจ ์งˆ๋ณ‘์ธ์ง€ ๋ชจ๋ฅผ ๋•Œ"๊ฐ€ ํ‰๊ท ์ ์ˆ˜ 3.168๋กœ์„œ ์„ธ๋ฒˆ์งธ๋กœ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 2 .์‘๋‹ต์ž ์ „์ฒด์˜ ๋ถˆ์•ˆ์ •๋„ ํ‰๊ท ์€ 2.077์ด์—ˆ๋‹ค. 3 .๋ถˆ์•ˆ์ •๋„์— ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ๋ถˆ์•ˆ์š”์ธ์˜ ๋ฒ”์ฃผ๋Š” "๋ณ‘์›์ƒํ™œ์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ" (r=.404, p<.001), "๋Œ€์ธ๊ด€๊ณ„์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ" (r=.365, p<.001), "์น˜๋ฃŒ์ž์ฒด์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"(r=.289, p<.001), "๊ฐ€์กฑ ๋ฐ ์žฅ๋ž˜๋ฌธ์ œ์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"(r=.244, p<.001) ์œผ๋กœ ๊ทœ๋ช…๋˜์—ˆ๋‹ค. 4. 4๊ฐœ๋ฒ”์ฃผ์™€ ๋ถˆ์•ˆ์ •๋„์™€์˜ ๋‹ค๋ณ€์ˆ˜ ์ƒ๊ด€์„ฑ์—์„œ๋Š” "๋ณ‘์› ์ƒํ™œ์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"๋งŒ์œผ๋กœ๋Š” 40.37%๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ (F=37.7676. p<.001), "๋Œ€์ธ๊ด€๊ณ„์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"์„ ์ฒจ๊ฐ€ํ•˜๋ฉด 41.19%๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ ( F=19.7220, p<.01), "์น˜๋ฃŒ์ž์ฒด์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"์„ ์ฒจ๊ฐ€ํ•˜๋ฉด 41.76%๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ (F=13.5177, p<.01), "๊ฐ€์กฑ ๋ฐ ์žฅ๋ž˜๋ฌธ์ œ์— ๋Œ€ํ•ด ๋Š๋ผ๋Š” ๋ถˆ์•ˆ"์„ ์ฒจ๊ฐ€ํ•˜๋ฉด 42.03 %๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. (F=10.2431, p<.05) 5. ํ™˜์ž๋“ค์ด ์ž…์›ํ•œ ์ƒํ™ฉ์—์„œ ๊ฒฝํ—˜ํ•˜๋Š” ๋ถˆ์•ˆ์˜ ์š”์ธ๊ณผ ์ผ๋ฐ˜์ ์ธ ํŠน์„ฑ ์‚ฌ์ด์—๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด์ง€ ์•Š์•˜์œผ๋ฉฐ (p>.05), ๋ถˆ์•ˆ์ •๋„์™€ ์ผ๋ฐ˜์ ์ธ ํŠน์„ฑ๊ณผ์˜ ๊ด€๊ณ„์—์„œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ธ ๊ฒƒ์œผ๋กœ๋Š” ์—ฐ๋ น ( x**2=51.954, p<.001). ๋ณต๋ฌด๊ธฐ๊ฐ„(x**2=31.916, p<.01), ์ž…์›๊ธฐ๊ฐ„(x**2=27.144, p<.01) ๋ฐ ์ž…์›ํ•œ ๋ณ‘๋™๋ณ„ (x**2=8.569, p<.05)์ด์—ˆ๋‹ค. [์˜๋ฌธ] The purpose of this study was to determine anxiety factors and levels of hospitalized soldiers and to examine this information in order to provide scientific data for solving and prevention of these problems. Data collection was done from April 24th to May 4th, 1984. The subjects were 196 soldiers who were hospitalized at there army hospitals located in forward areas. The research instruments were a 22 item questionnaire designed for measuring anxiety factors and Zung's Status Anxiety-lnventory, a 20 item questionnaire desired for checking the anxiety level of hospitalized soldiers. The data were interpreted by means of percentage, x**2_-test, Pearson's Correlation Coefficient and Stepwise Multiple Regress ton. The results were as follows; 1. The hospitalized soldier's mean score of anxiety factors was 2.897. The anxiety factors with highest scores were "The fear of problems in the family" (3.342)."Vacation canceled because of hospitalization" (3.250). "Unknown diaํ›„osis" (3.168). 2. The mean score of the anxiety levels of hospitalized soldiers was 2.077. 3. Among the 4 anxiety factor's categories, the level of anxiety differed as followed. a. "The anxiety of living in the hospital" (r=.404, p<.001) b. "The anxiety of interpersonal relationships" (r=.365, p<.001). c. "The anxiety of treatment procedure" (r=.289, p<.001). d. "The anxiety of problems in the family and future promition"(r=.244. p<.001). 4. The results of use of stepwise Multiple Regression to determine the difference between the categories and anxiety levels were as follows. "The anxiety of living in the hospital" accounted for 40.37% of the anxiety level (F=37.7676, p<.001), when the category of "The anxiety of interpersonal relationships" was added, it explained 41.19%. (F=19.7220, p<.01), when "The anxiety of treatment procedure" was included, it explained 41.76% (F=13.5177, p<.71) "The anxiety of problems in the family and future promotion", the total percentage was 42.31% (F=10.2431, p<.05). 5. The testing of the relationship between the hospitalized soldier's anxiety factors and general characteristics, showed no statistical significance. The testing of the relationship between the mean scores of the soldier's anxiety level and general characteristics were found to be significant in regard to age (x**2=31.961, p<.01), duration of one's hospitalization (x**2=27.144, p<.01) and hospitalized ward (x**2=18.019, p<.05).restrictio

    Time-step interleaved weight reuse for LSTM neural network computing

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    In Long Short-Term Memory (LSTM) neural network models, a weight matrix tends to be repeatedly loaded from DRAM if the size of on-chip storage of the processor is not large enough to store the entire matrix. To alleviate heavy overhead of DRAM access for weight loading in LSTM computations, we propose a weight reuse scheme which utilizes the weight sharing characteristics in two adjacent time-step computations. Experimental results show that the proposed weight reuse scheme reduces the energy consumption by 28.4-57.3% and increases the overall throughput by 110.8% compared to the conventional schemes.1

    Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators

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    The long short-term memory (LSTM) is a widely used neural network model for dealing with time-varying data. To reduce the memory requirement, pruning is often applied to the weight matrix of the LSTM, which makes the matrix sparse. In this paper, we present a new sparse matrix format, named rearranged compressed sparse column (RCSC), to maximize the inference speed of the LSTM hardware accelerator. The RCSC format speeds up the inference by: 1) evenly distributing the computation loads to processing elements (PEs) and 2) reducing the input vector load miss within the local buffer. We also propose a hardware architecture adopting hierarchical input buffer to further reduce the pipeline stalls which cannot be handled by the RCSC format alone. The simulation results for various datasets show that combined use of the RSCS format and the proposed hardware requires 2x smaller inference runtime on average compared to the previous work.11Nsciescopu
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